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diagnosis</title> <meta name="description" content="Search results for: motor fault diagnosis"> <meta name="keywords" content="motor fault diagnosis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="motor fault diagnosis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3369</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: motor fault diagnosis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3369</span> Motor Gear Fault Diagnosis by Measurement of Current, Noise and Vibration on AC Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun-Ki%20Hong">Sun-Ki Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki-Seok%20Kim"> Ki-Seok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Ho%20Jo"> Yong-Ho Jo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lots of motors have been being used in industry. Therefore many researchers have studied about the failure diagnosis of motors. In this paper, the effect of measuring environment for diagnosis of gear fault connected to a motor shaft is studied. The fault diagnosis is executed through the comparison of normal gear and abnormal gear. The measured FFT data are compared with the normal data and analyzed for q-axis current, noise and vibration. For bad and good environment, the diagnosis results are compared. From these, it is shown that the bad measuring environment may not be able to detect exactly the motor gear fault. Therefore it is emphasized that the measuring environment should be carefully prepared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motor%20fault" title="motor fault">motor fault</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=FFT" title=" FFT"> FFT</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration" title=" vibration"> vibration</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=q-axis%20current" title=" q-axis current"> q-axis current</a>, <a href="https://publications.waset.org/abstracts/search?q=measuring%20environment" title=" measuring environment"> measuring environment</a> </p> <a href="https://publications.waset.org/abstracts/32684/motor-gear-fault-diagnosis-by-measurement-of-current-noise-and-vibration-on-ac-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32684.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">558</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3368</span> Fault Diagnosis in Induction Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kirti%20Gosavi">Kirti Gosavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Anita%20Bhole"> Anita Bhole</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper demonstrates simulation and steady-state performance of three phase squirrel cage induction motor and detection of rotor broken bar fault using MATLAB. This simulation model is successfully used in the fault detection of rotor broken bar for the induction machines. A dynamic model using PWM inverter and mathematical modelling of the motor is developed. The dynamic simulation of the small power induction motor is one of the key steps in the validation of the design process of the motor drive system and it is needed for eliminating advertent design errors and the resulting error in the prototype construction and testing. The simulation model will be helpful in detecting the faults in three phase induction motor using Motor current signature analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=squirrel%20cage%20induction%20motor" title="squirrel cage induction motor">squirrel cage induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=pulse%20width%20modulation%20%28PWM%29" title=" pulse width modulation (PWM)"> pulse width modulation (PWM)</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title=" induction motor"> induction motor</a> </p> <a href="https://publications.waset.org/abstracts/22499/fault-diagnosis-in-induction-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">633</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3367</span> Static Eccentricity Fault Diagnosis in Synchronous Reluctance Motor and Permanent Magnet Assisted Synchronous Reluctance Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Naeimi">M. Naeimi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Aghazadeh"> H. Aghazadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Afjei"> E. Afjei</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Siadatan"> A. Siadatan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a novel view of air gap magnetic field analysis of synchronous reluctance motor and permanent magnet assisted synchronous reluctance motor under static eccentricity to provide the precise fault diagnosis based on three-dimensional finite element method is presented. Analytical nature of this method makes it possible to simulate reliable and precise model by considering the end effects and axial fringing effects. The results of the three-dimensional finite element analysis of synchronous reluctance motor and permanent magnet synchronous reluctance motor such as flux linkage, flux density, and compression both of SynRM and PM-SynRM for various eccentric motor conditions are obtained and analyzed. These results present useful information regarding to the detection of static eccentricity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synchronous%20reluctance%20motor%20%28SynRM%29" title="synchronous reluctance motor (SynRM)">synchronous reluctance motor (SynRM)</a>, <a href="https://publications.waset.org/abstracts/search?q=permanent%20magnet%20assisted%20synchronous%20reluctance%20motor%20%28PMaSynRM%29" title=" permanent magnet assisted synchronous reluctance motor (PMaSynRM)"> permanent magnet assisted synchronous reluctance motor (PMaSynRM)</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20eccentricity" title=" static eccentricity"> static eccentricity</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20analysis" title=" fault analysis"> fault analysis</a> </p> <a href="https://publications.waset.org/abstracts/87636/static-eccentricity-fault-diagnosis-in-synchronous-reluctance-motor-and-permanent-magnet-assisted-synchronous-reluctance-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3366</span> Comparison of Techniques for Detection and Diagnosis of Eccentricity in the Air-Gap Fault in Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abrah%C3%A3o%20S.%20Fontes">Abrah茫o S. Fontes</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20V.%20Cardoso"> Carlos A. V. Cardoso</a>, <a href="https://publications.waset.org/abstracts/search?q=Levi%20P.%20B.%20Oliveira"> Levi P. B. Oliveira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this article, these techniques are applied and compared on a real motor, which has the fault of eccentricity in the air-gap. It was used as a theoretical model of an electric induction motor without fault in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the fault of eccentricity in the air gap, whose comparison between these showed the suitability of each one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eccentricity%20in%20the%20air-gap" title="eccentricity in the air-gap">eccentricity in the air-gap</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=induction%20motors" title=" induction motors"> induction motors</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title=" predictive maintenance"> predictive maintenance</a> </p> <a href="https://publications.waset.org/abstracts/62315/comparison-of-techniques-for-detection-and-diagnosis-of-eccentricity-in-the-air-gap-fault-in-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62315.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3365</span> Modelling and Detecting the Demagnetization Fault in the Permanent Magnet Synchronous Machine Using the Current Signature Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yassa%20Nacera">Yassa Nacera</a>, <a href="https://publications.waset.org/abstracts/search?q=Badji%20Abderrezak"> Badji Abderrezak</a>, <a href="https://publications.waset.org/abstracts/search?q=Saidoune%20Abdelmalek"> Saidoune Abdelmalek</a>, <a href="https://publications.waset.org/abstracts/search?q=Houassine%20Hamza"> Houassine Hamza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several kinds of faults can occur in a permanent magnet synchronous machine (PMSM) systems: bearing faults, electrically short/open faults, eccentricity faults, and demagnetization faults. Demagnetization fault means that the strengths of permanent magnets (PM) in PMSM decrease, and it causes low output torque, which is undesirable for EVs. The fault is caused by physical damage, high-temperature stress, inverse magnetic field, and aging. Motor current signature analysis (MCSA) is a conventional motor fault detection method based on the extraction of signal features from stator current. a simulation model of the PMSM under partial demagnetization and uniform demagnetization fault was established, and different degrees of demagnetization fault were simulated. The harmonic analyses using the Fast Fourier Transform (FFT) show that the fault diagnosis method based on the harmonic wave analysis is only suitable for partial demagnetization fault of the PMSM and does not apply to uniform demagnetization fault of the PMSM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=permanent%20magnet" title="permanent magnet">permanent magnet</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=demagnetization" title=" demagnetization"> demagnetization</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a> </p> <a href="https://publications.waset.org/abstracts/182275/modelling-and-detecting-the-demagnetization-fault-in-the-permanent-magnet-synchronous-machine-using-the-current-signature-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182275.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">68</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3364</span> Online Electric Current Based Diagnosis of Stator Faults on Squirrel Cage Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20Paz%20Parra">Alejandro Paz Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Luis%20Oslinger%20Gutierrez"> Jose Luis Oslinger Gutierrez</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Olaya%20Ochoa"> Javier Olaya Ochoa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present paper, five electric current based methods to analyze electric faults on the stator of induction motors (IM) are used and compared. The analysis tries to extend the application of the multiple reference frames diagnosis technique. An eccentricity indicator is presented to improve the application of the Park鈥檚 Vector Approach technique. Most of the fault indicators are validated and some others revised, agree with the technical literatures and published results. A tri-phase 3hp squirrel cage IM, especially modified to establish different fault levels, is used for validation purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motor%20fault%20diagnosis" title="motor fault diagnosis">motor fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title=" induction motor"> induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=MCSA" title=" MCSA"> MCSA</a>, <a href="https://publications.waset.org/abstracts/search?q=ESA" title=" ESA"> ESA</a>, <a href="https://publications.waset.org/abstracts/search?q=Extended%20Park%C2%B4s%20vector%20approach" title=" Extended Park麓s vector approach"> Extended Park麓s vector approach</a>, <a href="https://publications.waset.org/abstracts/search?q=multiparameter%20analysis" title=" multiparameter analysis"> multiparameter analysis</a> </p> <a href="https://publications.waset.org/abstracts/57385/online-electric-current-based-diagnosis-of-stator-faults-on-squirrel-cage-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57385.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3363</span> Induction Motor Eccentricity Fault Recognition Using Rotor Slot Harmonic with Stator Current Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nouredine%20Benouzza">Nouredine Benouzza</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Hamida%20Boudinar"> Ahmed Hamida Boudinar</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeddine%20Bendiabdellah"> Azeddine Bendiabdellah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An algorithm for Eccentricity Fault Detection (EFD) applied to a squirrel cage induction machine is proposed in this paper. This algorithm employs the behavior of the stator current spectral analysis and the localization of the Rotor Slot Harmonic (RSH) frequency to detect eccentricity faults in three phase induction machine. The RHS frequency once obtained is used as a key parameter into a simple developed expression to directly compute the eccentricity fault frequencies in the induction machine. Experimental tests performed for both a healthy motor and a faulty motor with different eccentricity fault severities illustrate the effectiveness and merits of the proposed EFD algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=squirrel%20cage%20motor" title="squirrel cage motor">squirrel cage motor</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=eccentricity%20faults" title=" eccentricity faults"> eccentricity faults</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20spectral%20analysis" title=" current spectral analysis"> current spectral analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor%20slot%20harmonic" title=" rotor slot harmonic"> rotor slot harmonic</a> </p> <a href="https://publications.waset.org/abstracts/71112/induction-motor-eccentricity-fault-recognition-using-rotor-slot-harmonic-with-stator-current-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3362</span> Application of Neural Petri Net to Electric Control System Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadiq%20J.%20Abou-Loukh">Sadiq J. Abou-Loukh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=petri%20net" title="petri net">petri net</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20petri%20net" title=" neural petri net"> neural petri net</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20control%20system" title=" electric control system"> electric control system</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/16653/application-of-neural-petri-net-to-electric-control-system-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3361</span> Diagnosis Of Static, Dynamic, And Mixed Eccentricity In Line Start Permanent Magnet Synchronous Motor By Using FEM </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Moustafa%20Mahmoud%20Sedky">Mohamed Moustafa Mahmoud Sedky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In line start permanent magnet synchronous motor, eccentricity is a common fault that can make it necessary to remove the motor from the production line. However, because the motor may be inaccessible, diagnosing the fault is not easy. This paper presents an FEM that identifies different models, static eccentricity, dynamic eccentricity, and mixed eccentricity, at no load and full load. The method overcomes the difficulty of applying FEMs to transient behavior. It simulates motor speed, torque and flux density distribution along the air gap for SE, DE, and ME. This paper represents the various effects of different eccentricities types on the transient performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=line%20start%20permanent%20magnet" title="line start permanent magnet">line start permanent magnet</a>, <a href="https://publications.waset.org/abstracts/search?q=synchronous%20machine" title=" synchronous machine"> synchronous machine</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20eccentricity" title=" static eccentricity"> static eccentricity</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20eccentricity" title=" dynamic eccentricity"> dynamic eccentricity</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20eccentricity" title=" mixed eccentricity"> mixed eccentricity</a> </p> <a href="https://publications.waset.org/abstracts/4065/diagnosis-of-static-dynamic-and-mixed-eccentricity-in-line-start-permanent-magnet-synchronous-motor-by-using-fem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4065.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3360</span> Fault Diagnosis of Squirrel-Cage Induction Motor by a Neural Network Multi-Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yahia.%20Kourd">Yahia. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guersi%20D.%20Lefebvre"> N. Guersi D. Lefebvre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=faults%20diagnosis" title="faults diagnosis">faults diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-models" title=" multi-models"> multi-models</a>, <a href="https://publications.waset.org/abstracts/search?q=squirrel-cage%20induction%20motor" title=" squirrel-cage induction motor"> squirrel-cage induction motor</a> </p> <a href="https://publications.waset.org/abstracts/8200/fault-diagnosis-of-squirrel-cage-induction-motor-by-a-neural-network-multi-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8200.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">636</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3359</span> Induction Motor Stator Fault Analysis Using Phase-Angle and Magnitude of the Line Currents Spectra</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Hamida%20Boudinar">Ahmed Hamida Boudinar</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Benouzza"> Noureddine Benouzza</a>, <a href="https://publications.waset.org/abstracts/search?q=Azeddine%20Bendiabdellah"> Azeddine Bendiabdellah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Amine%20Khodja"> Mohamed El Amine Khodja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a new diagnosis approach for identification of the progressive stator winding inter-turn short-circuit fault in induction motor. This approach is based on a simple monitoring of the combined information related to both magnitude and phase-angle obtained from the fundamental by the three line currents frequency analysis. In addition, to simplify the interpretation and analysis of the data; a new graphical tool based on a triangular representation is suggested. This representation, depending on its size, enables to visualize in a simple and clear manner, the existence of the stator inter-turn short-circuit fault and its discrimination with respect to a healthy stator. Experimental results show well the benefit and effectiveness of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title="induction motor">induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=magnitude" title=" magnitude"> magnitude</a>, <a href="https://publications.waset.org/abstracts/search?q=phase-angle" title=" phase-angle"> phase-angle</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20analysis" title=" spectral analysis"> spectral analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stator%20fault" title=" stator fault"> stator fault</a> </p> <a href="https://publications.waset.org/abstracts/71288/induction-motor-stator-fault-analysis-using-phase-angle-and-magnitude-of-the-line-currents-spectra" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71288.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3358</span> Current-Based Multiple Faults Detection in Electrical Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moftah%20BinHasan">Moftah BinHasan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Induction motors (IM) are vital components in industrial processes whose failure may yield to an unexpected interruption at the industrial plant, with highly incurred consequences in costs, product quality, and safety. Among different detection approaches proposed in the literature, that based on stator current monitoring termed as Motor Current Signature Analysis (MCSA) is the most preferred. MCSA is advantageous due to its non-invasive properties. The popularity of motor current signature analysis comes from being that the current consists of motor harmonics, around the supply frequency, which show some properties related to different situations of healthy and faulty conditions. One of the techniques used with machine line current resorts to spectrum analysis. Besides discussing the fundamentals of MCSA and its applications in the condition monitoring arena, this paper shows a summary of the most frequent faults and their consequence signatures on the stator current spectrum of an induction motor. In addition, this article presents different case studies of induction motor fault diagnosis. These faults were seeded in the machine which was run for more than an hour for each test before the results were recorded for the faulty situations. These results are then compared with those for the healthy cases that were recorded earlier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title="induction motor">induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=MCSA" title=" MCSA"> MCSA</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor" title=" rotor"> rotor</a>, <a href="https://publications.waset.org/abstracts/search?q=stator" title=" stator"> stator</a>, <a href="https://publications.waset.org/abstracts/search?q=bearing" title=" bearing"> bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=eccentricity" title=" eccentricity"> eccentricity</a> </p> <a href="https://publications.waset.org/abstracts/24001/current-based-multiple-faults-detection-in-electrical-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24001.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">459</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3357</span> Multiple Fault Detection and Classification in a Coupled Motor with Rotor Using Artificial Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Nouri%20Khajavi">Mehrdad Nouri Khajavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gollamhassan%20%20Payganeh"> Gollamhassan Payganeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Fallah%20Tafti"> Mohsen Fallah Tafti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unbalance" title="unbalance">unbalance</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20misalignment" title=" parallel misalignment"> parallel misalignment</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20faults" title=" combined faults"> combined faults</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20signals" title=" vibration signals"> vibration signals</a> </p> <a href="https://publications.waset.org/abstracts/59244/multiple-fault-detection-and-classification-in-a-coupled-motor-with-rotor-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3356</span> Actuator Fault Detection and Fault Tolerant Control of a Nonlinear System Using Sliding Mode Observer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Loukil">R. Loukil</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Chtourou"> M. Chtourou</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Damak"> T. Damak </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20isolation%20FDI" title="fault detection and isolation FDI">fault detection and isolation FDI</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tolerant%20control%20FTC" title=" fault tolerant control FTC"> fault tolerant control FTC</a>, <a href="https://publications.waset.org/abstracts/search?q=sliding%20mode%20observer" title=" sliding mode observer"> sliding mode observer</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20system" title=" nonlinear system"> nonlinear system</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a> </p> <a href="https://publications.waset.org/abstracts/41716/actuator-fault-detection-and-fault-tolerant-control-of-a-nonlinear-system-using-sliding-mode-observer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3355</span> Fourier Transform and Machine Learning Techniques for Fault Detection and Diagnosis of Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duc%20V.%20Nguyen">Duc V. Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Induction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where the high cost of a continuous monitoring system can be Justified. Nevertheless, induced current monitoring can be implemented inexpensively on machines with arbitrary sizes by using current transformers. In this regard, effective and low-cost fault detection techniques can be implemented, hence reducing the maintenance and downtime costs of motors. This work proposes a method for fault detection and diagnosis of induction motors, which combines classical fast Fourier transform and modern/advanced machine learning techniques. The proposed method is validated on real-world data and achieves a precision of 99.7% for fault detection and 100% for fault classification with minimal expert knowledge requirement. In addition, this approach allows users to be able to optimize/balance risks and maintenance costs to achieve the highest benet based on their requirements. These are the key requirements of a robust prognostics and health management system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=FFT" title=" FFT"> FFT</a>, <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title=" induction motor"> induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title=" predictive maintenance"> predictive maintenance</a> </p> <a href="https://publications.waset.org/abstracts/134923/fourier-transform-and-machine-learning-techniques-for-fault-detection-and-diagnosis-of-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134923.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3354</span> Asynchronous Sequential Machines with Fault Detectors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seong%20Woo%20Kwak">Seong Woo Kwak</a>, <a href="https://publications.waset.org/abstracts/search?q=Jung-Min%20Yang"> Jung-Min Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A strategy of fault diagnosis and tolerance for asynchronous sequential machines is discussed in this paper. With no synchronizing clock, it is difficult to diagnose an occurrence of permanent or stuck-in faults in the operation of asynchronous machines. In this paper, we present a fault detector comprised of a timer and a set of static functions to determine the occurrence of faults. In order to realize immediate fault tolerance, corrective control theory is applied to designing a dynamic feedback controller. Existence conditions for an appropriate controller and its construction algorithm are presented in terms of reachability of the machine and the feature of fault occurrences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asynchronous%20sequential%20machines" title="asynchronous sequential machines">asynchronous sequential machines</a>, <a href="https://publications.waset.org/abstracts/search?q=corrective%20control" title=" corrective control"> corrective control</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis%20and%20tolerance" title=" fault diagnosis and tolerance"> fault diagnosis and tolerance</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20detector" title=" fault detector"> fault detector</a> </p> <a href="https://publications.waset.org/abstracts/53634/asynchronous-sequential-machines-with-fault-detectors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53634.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3353</span> Diagnosis of Induction Machine Faults by DWT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Akbari">Hamidreza Akbari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, for detection of inclined eccentricity in an induction motor, time鈥揻requency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20machine" title="induction machine">induction machine</a>, <a href="https://publications.waset.org/abstracts/search?q=fault" title=" fault"> fault</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=electric" title=" electric"> electric</a> </p> <a href="https://publications.waset.org/abstracts/13400/diagnosis-of-induction-machine-faults-by-dwt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13400.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3352</span> On the Representation of Actuator Faults Diagnosis and Systems Invertibility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Sallem">F. Sallem</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Dahhou"> B. Dahhou</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kamoun"> A. Kamoun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, the main problem considered is the detection and the isolation of the actuator fault. A new formulation of the linear system is generated to obtain the conditions of the actuator fault diagnosis. The proposed method is based on the representation of the actuator as a subsystem connected with the process system in cascade manner. The designed formulation is generated to obtain the conditions of the actuator fault detection and isolation. Detectability conditions are expressed in terms of the invertibility notions. An example and a comparative analysis with the classic formulation illustrate the performances of such approach for simple actuator fault diagnosis by using the linear model of nuclear reactor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=actuator%20fault" title="actuator fault">actuator fault</a>, <a href="https://publications.waset.org/abstracts/search?q=Fault%20detection" title=" Fault detection"> Fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=left%20invertibility" title=" left invertibility"> left invertibility</a>, <a href="https://publications.waset.org/abstracts/search?q=nuclear%20reactor" title=" nuclear reactor"> nuclear reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=observability" title=" observability"> observability</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20intervals" title=" parameter intervals"> parameter intervals</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20inversion" title=" system inversion"> system inversion</a> </p> <a href="https://publications.waset.org/abstracts/5525/on-the-representation-of-actuator-faults-diagnosis-and-systems-invertibility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3351</span> Fault Diagnosis of Nonlinear Systems Using Dynamic Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Sobhani-Tehrani">E. Sobhani-Tehrani</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Khorasani"> K. Khorasani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Meskin"> N. Meskin </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPE) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. Two NPE structures including series-parallel and parallel are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the NPEs to systems with partial-state measurement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fault%20diagnosis" title="hybrid fault diagnosis">hybrid fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20neural%20networks" title=" dynamic neural networks"> dynamic neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tolerant%20observer" title=" fault tolerant observer"> fault tolerant observer</a> </p> <a href="https://publications.waset.org/abstracts/23088/fault-diagnosis-of-nonlinear-systems-using-dynamic-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23088.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3350</span> Observer-based Robust Diagnosis for Wind Turbine System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Odofin">Sarah Odofin</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiwei%20Gao"> Zhiwei Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Operations and maintenance of wind turbine have received much attention by researcher due to rapid expansion of wind farms. This paper explores a novel fault diagnosis that is designed and optimized to be very sensitive to faults and robust to disturbances. The faults considered are the sensor faults of which the augmented observer is considered to enlarge faults and to be robust to disturbance. A qualitative model based analysis is proposed for early fault diagnosis to minimize downtime mostly caused by components breakdown and exploit productivity. Simulation results are computed validating the models provided which demonstrates system performance using practical application of fault type examples. The results demonstrate the effectiveness of the developed techniques investigated in a Matlab/Simulink environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20turbine" title="wind turbine">wind turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=augmented%20observer" title=" augmented observer"> augmented observer</a>, <a href="https://publications.waset.org/abstracts/search?q=disturbance%20robustness" title=" disturbance robustness"> disturbance robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20estimation" title=" fault estimation"> fault estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20monitoring" title=" sensor monitoring"> sensor monitoring</a> </p> <a href="https://publications.waset.org/abstracts/18297/observer-based-robust-diagnosis-for-wind-turbine-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18297.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3349</span> Fault Diagnosis in Induction Motors Using the Discrete Wavelet Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Yahia">Khaled Yahia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20park%E2%80%99s%20vector%20modulus%20%28CPVM%29" title=" current park鈥檚 vector modulus (CPVM) "> current park鈥檚 vector modulus (CPVM) </a> </p> <a href="https://publications.waset.org/abstracts/31450/fault-diagnosis-in-induction-motors-using-the-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31450.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">569</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3348</span> Variation of Inductance in a Switched-Reluctance Motor under Various Rotor Faults</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Asghar%20Saqib">Muhammad Asghar Saqib</a>, <a href="https://publications.waset.org/abstracts/search?q=Saad%20Saleem%20Khan"> Saad Saleem Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Abdul%20Rahman%20Kashif"> Syed Abdul Rahman Kashif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to have higher efficiency, performance and reliability the regular monitoring of an electrical motor is required. This article presents a novel view of the air-gap magnetic field analysis of a switched reluctance motor under rotor cracks and rotor tilt along its shaft axis. The fault diagnosis is illustrated on the basis of a 3-D model of the motor using finite element analysis (FEA). The analytical equations of flux linkages have been used to determine the inductance. The results of the 3-D finite element analysis on a 6/4 switched reluctance motor (SRM) shows the variation of mutual inductance with the tilting of the rotor shaft and cracked rotor conditions. These results present useful information regarding the detection of shaft tilting and cracked rotors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=switched%20reluctance%20motor" title="switched reluctance motor">switched reluctance motor</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cracked%20rotor" title=" cracked rotor"> cracked rotor</a>, <a href="https://publications.waset.org/abstracts/search?q=3-D%20modelling%20of%20a%20srm" title=" 3-D modelling of a srm"> 3-D modelling of a srm</a> </p> <a href="https://publications.waset.org/abstracts/30951/variation-of-inductance-in-a-switched-reluctance-motor-under-various-rotor-faults" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30951.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">665</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3347</span> Fault Diagnosis in Induction Motors Using Discrete Wavelet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Titaouine"> A. Titaouine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ghoggal"> A. Ghoggal</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20E.%20Zouzou"> S. E. Zouzou</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Benchabane"> F. Benchabane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Induction%20Motors%20%28IMs%29" title="Induction Motors (IMs)">Induction Motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park鈥檚 Vector Modulus (CPVM)"> Current Park鈥檚 Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/22046/fault-diagnosis-in-induction-motors-using-discrete-wavelet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22046.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">553</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3346</span> Application of Local Mean Decomposition for Rolling Bearing Fault Diagnosis Based On Vibration Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Toufik%20Bensana">Toufik Bensana</a>, <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Mekhilef"> Slimane Mekhilef</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Tadjine"> Kamel Tadjine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration analysis has been frequently applied in the condition monitoring and fault diagnosis of rolling element bearings. Unfortunately, the vibration signals collected from a faulty bearing are generally non stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title="fault diagnosis">fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title=" condition monitoring"> condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20mean%20decomposition" title=" local mean decomposition"> local mean decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearing" title=" rolling element bearing"> rolling element bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20analysis" title=" vibration analysis"> vibration analysis</a> </p> <a href="https://publications.waset.org/abstracts/31381/application-of-local-mean-decomposition-for-rolling-bearing-fault-diagnosis-based-on-vibration-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31381.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">397</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3345</span> Root Mean Square-Based Method for Fault Diagnosis and Fault Detection and Isolation of Current Fault Sensor in an Induction Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Akrad">Ahmad Akrad</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Sehab"> Rabia Sehab</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Alyoussef"> Fadi Alyoussef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20machine" title="induction machine">induction machine</a>, <a href="https://publications.waset.org/abstracts/search?q=asymmetric%20nonlinear%20model" title=" asymmetric nonlinear model"> asymmetric nonlinear model</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuit%20fault" title=" inter-turn short-circuit fault"> inter-turn short-circuit fault</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square" title=" root mean square"> root mean square</a>, <a href="https://publications.waset.org/abstracts/search?q=current%20sensor%20fault" title=" current sensor fault"> current sensor fault</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20isolation" title=" fault detection and isolation"> fault detection and isolation</a> </p> <a href="https://publications.waset.org/abstracts/133081/root-mean-square-based-method-for-fault-diagnosis-and-fault-detection-and-isolation-of-current-fault-sensor-in-an-induction-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133081.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3344</span> Stator Short-Circuits Fault Diagnosis in Induction Motors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Yahia">K. Yahia</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sahraoui"> M. Sahraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Guettaf"> A. Guettaf </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park鈥檚 vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental results, show the effectiveness of the used method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motors%20%28IMs%29" title="induction motors (IMs)">induction motors (IMs)</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-turn%20short-circuits%20diagnosis" title=" inter-turn short-circuits diagnosis"> inter-turn short-circuits diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform%20%28DWT%29" title=" discrete wavelet transform (DWT)"> discrete wavelet transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Current%20Park%E2%80%99s%20Vector%20Modulus%20%28CPVM%29" title=" Current Park鈥檚 Vector Modulus (CPVM)"> Current Park鈥檚 Vector Modulus (CPVM)</a> </p> <a href="https://publications.waset.org/abstracts/82115/stator-short-circuits-fault-diagnosis-in-induction-motors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3343</span> Dissolved Gas Analysis Based Regression Rules from Trained ANN for Transformer Fault Diagnosis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepika%20Bhalla">Deepika Bhalla</a>, <a href="https://publications.waset.org/abstracts/search?q=Raj%20Kumar%20Bansal"> Raj Kumar Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hari%20Om%20Gupta"> Hari Om Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=dissolved%20gas%20analysis" title=" dissolved gas analysis"> dissolved gas analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=rules%20extraction" title=" rules extraction"> rules extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/14091/dissolved-gas-analysis-based-regression-rules-from-trained-ann-for-transformer-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14091.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">536</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3342</span> On Fault Diagnosis of Asynchronous Sequential Machines with Parallel Composition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jung-Min%20Yang">Jung-Min Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fault diagnosis of composite asynchronous sequential machines with parallel composition is addressed in this paper. An adversarial input can infiltrate one of two submachines comprising the composite asynchronous machine, causing an unauthorized state transition. The objective is to characterize the condition under which the controller can diagnose any fault occurrence. Two control configurations, state feedback and output feedback, are considered in this paper. In the case of output feedback, the exact estimation of the state is impossible since the current state is inaccessible and the output feedback is given as the form of burst. A simple example is provided to demonstrate the proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asynchronous%20sequential%20machines" title="asynchronous sequential machines">asynchronous sequential machines</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20composition" title=" parallel composition"> parallel composition</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=corrective%20control" title=" corrective control"> corrective control</a> </p> <a href="https://publications.waset.org/abstracts/76606/on-fault-diagnosis-of-asynchronous-sequential-machines-with-parallel-composition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76606.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3341</span> Experimental Set-Up for Investigation of Fault Diagnosis of a Centrifugal Pump</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maamar%20Ali%20Saud%20Al%20Tobi">Maamar Ali Saud Al Tobi</a>, <a href="https://publications.waset.org/abstracts/search?q=Geraint%20Bevan"> Geraint Bevan</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Ramachandran"> K. P. Ramachandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Wallace"> Peter Wallace</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Harrison"> David Harrison</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=centrifugal%20pump%20setup" title="centrifugal pump setup">centrifugal pump setup</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20analysis" title=" vibration analysis"> vibration analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/66155/experimental-set-up-for-investigation-of-fault-diagnosis-of-a-centrifugal-pump" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3340</span> Robust Fault Diagnosis for Wind Turbine Systems Subjected to Multi-Faults</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Odofin">Sarah Odofin</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiwei%20Gao"> Zhiwei Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun%20Kai"> Sun Kai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Operations, maintenance and reliability of wind turbines have received much attention over the years due to rapid expansion of wind farms. This paper explores early fault diagnosis scale technique based on a unique scheme of a 5MW wind turbine system that is optimized by genetic algorithm to be very sensitive to faults and resilient to disturbances. A quantitative model based analysis is pragmatic for primary fault diagnosis monitoring assessment to minimize downtime mostly caused by components breakdown and exploit productivity consistency. Simulation results are computed validating the wind turbine model which demonstrates system performance in a practical application of fault type examples. The results show the satisfactory effectiveness of the applied performance investigated in a Matlab/Simulink/Gatool environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disturbance%20robustness" title="disturbance robustness">disturbance robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20monitoring%20and%20detection" title=" fault monitoring and detection"> fault monitoring and detection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=observer%20technique" title=" observer technique"> observer technique</a> </p> <a href="https://publications.waset.org/abstracts/19745/robust-fault-diagnosis-for-wind-turbine-systems-subjected-to-multi-faults" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">380</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=motor%20fault%20diagnosis&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=motor%20fault%20diagnosis&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=motor%20fault%20diagnosis&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=motor%20fault%20diagnosis&page=5">5</a></li> <li class="page-item"><a class="page-link" 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