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In this paper we propose new feature selection methods by combining between relief, mutual information and sequential selection. The new approach is compared with other existing and we demonstrate some improvement when they are applied to a random dataset and on real data acquired from wind turbine bearings aiming to detect fault in the turbine using vibration signal.","publication_date":"2015,,","publication_name":"International Journal of Computer Science and Application","grobid_abstract_attachment_id":"101861009"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [237590476]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":101861009,"attachmentType":"pdf"}"><img alt="First page of “International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/101861009/mini_magick20230505-1-1yzg69.png?1683275872" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">International Journal of Computer Science and Application , Vol. 4, No. 1—April 2015 1 2324 ‐ 7037/15/01 001 ‐ 08 © 2015 DEStech Publications, Inc. doi: 10.12783/ijcsa.2015.0401.01 Feature Selection Algorithm Used to Classify Faults in Turbine Bearings</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="237590476" href="https://independent.academia.edu/hokoyo"><img alt="Profile image of henry okoyo" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />henry okoyo</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2015, International Journal of Computer Science and Application</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">8 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 101274682; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Feature Selection is a very important step that select a few number of feature used for the classification in order to reduce execution time, to improve accuracy and to enhance performance of the identification system. In this paper we propose new feature selection methods by combining between relief, mutual information and sequential selection. The new approach is compared with other existing and we demonstrate some improvement when they are applied to a random dataset and on real data acquired from wind turbine bearings aiming to detect fault in the turbine using vibration signal.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":101861009,"attachmentType":"pdf","workUrl":"https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":101861009,"attachmentType":"pdf","workUrl":"https://www.academia.edu/101274682/International_Journal_of_Computer_Science_and_Application_Vol_4_No_1_April_2015_1_2324_7037_15_01_001_08_2015_DEStech_Publications_Inc_doi_10_12783_ijcsa_2015_0401_01_Feature_Selection_Algorithm_Used_to_Classify_Faults_in_Turbine_Bearings"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="101274682" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high falsealarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis","attachmentId":44711310,"attachmentType":"pdf","work_url":"https://www.academia.edu/14007894/A_hybrid_feature_selection_scheme_for_unsupervised_learning_and_its_application_in_bearing_fault_diagnosis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14007894/A_hybrid_feature_selection_scheme_for_unsupervised_learning_and_its_application_in_bearing_fault_diagnosis"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="123875833" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/123875833/A_methodological_approach_for_detecting_multiple_faults_in_wind_turbine_blades_based_on_vibration_signals_and_machine_learning">A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="138620598" href="https://itswtech.academia.edu/MohsinNoori">Mohsin N Hamzah</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Curved and Layered Structures, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Wind turbines generate clean and renewable energy for the international market. The most important aspect of wind turbine maintenance is reducing failures, downtime, and operating and maintenance expenses. This study aims to detect multiple faults exhibited by wind turbine blades; failures such as cracks (tip crack, mid-span crack, and crack near the root) were observed in the blades at different locations. The research suggests a new approach, incorporating vibration signals and machine learning techniques to identify various failures in wind turbine blades. The technology of ranking features such as ReliefF algorithms, chi-squares, and information gains was adopted to discuss a method framework to diagnose several problems in wind turbine blades, such as cracks in different locations. The k-nearest neighbors (KNNs), support vector machines, and random forests are used to classify data based on measured vibration signals. The eight main time-domain features are calculated from the vibration signals. The proposed methodology was validated using four databases. The results showed good classification accuracy in four databases, with at least three non-conventional features in each database's top nine features of the three classification techniques. The results also showed that when the ReliefF selection algorithm is applied with the KNN classification algorithm, it generates the highest classification accuracy under all failure conditions, and the value is 97%. Finally, the performance of the proposed classification model is compared with other machine learning classification models, and a promising result is obtained.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A methodological approach for detecting multiple faults in wind turbine blades based on vibration signals and machine learning","attachmentId":118208596,"attachmentType":"pdf","work_url":"https://www.academia.edu/123875833/A_methodological_approach_for_detecting_multiple_faults_in_wind_turbine_blades_based_on_vibration_signals_and_machine_learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/123875833/A_methodological_approach_for_detecting_multiple_faults_in_wind_turbine_blades_based_on_vibration_signals_and_machine_learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="56004899" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56004899/Bearing_Health_Monitoring_Using_Relief_F_Based_Feature_Relevance_Analysis_and_HMM">Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="183148607" href="https://independent.academia.edu/MauricioHolguinLondono">Mauricio Holguin Londono</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied Sciences</p><p class="ds-related-work--abstract ds2-5-body-sm">Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM","attachmentId":71603912,"attachmentType":"pdf","work_url":"https://www.academia.edu/56004899/Bearing_Health_Monitoring_Using_Relief_F_Based_Feature_Relevance_Analysis_and_HMM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/56004899/Bearing_Health_Monitoring_Using_Relief_F_Based_Feature_Relevance_Analysis_and_HMM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="118383600" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/118383600/A_Hybrid_Feature_Selection_and_Construction_Method_for_Detection_of_Wind_Turbine_Generator_Heating_Faults">A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9405033" href="https://independent.academia.edu/BurakBarutcu">Burak Barutcu</a></div><p class="ds-related-work--metadata ds2-5-body-xs">arXiv (Cornell University), 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">Preprocessing of information is an essential step for the effective design of machine learning applications. Feature construction and selection are powerful techniques used for this aim. In this paper, a feature selection and construction approach is presented for the detection of wind turbine generator heating faults. Data were collected from Supervisory Control and Data Acquisition (SCADA) system of a wind turbine. The original features directly collected from the data collection system consist of wind characteristics, operational data, temperature measurements and status information. In addition to these original features, new features were created in the feature construction step to obtain information that can be more powerful indications of the faults. After the construction of new features, a hybrid feature selection technique was implemented to find out the most relevant features in the overall set to increase the classification accuracy and decrease the computational burden. Feature selection step consists of filter and wrapper-based parts. Filter based feature selection was applied to exclude the features which are non-discriminative and wrapper-based method was used to determine the final features considering the redundancies and mutual relations amongst them. Artificial Neural Networks were used both in the detection phase and as the induction algorithm of the wrapper-based feature selection part. The results show that, the proposed approach contributes to the fault detection system to be more reliable especially in terms of reducing the number of false fault alarms.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults","attachmentId":114023397,"attachmentType":"pdf","work_url":"https://www.academia.edu/118383600/A_Hybrid_Feature_Selection_and_Construction_Method_for_Detection_of_Wind_Turbine_Generator_Heating_Faults","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118383600/A_Hybrid_Feature_Selection_and_Construction_Method_for_Detection_of_Wind_Turbine_Generator_Heating_Faults"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="58295992" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/58295992/Fault_detection_and_identification_with_a_new_feature_selection_based_on_mutual_information">Fault detection and identification with a new feature selection based on mutual information</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="86393988" href="https://independent.academia.edu/TeodorTiplica">Teodor Tiplica</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Process Control, 2008</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a fault diagnosis procedure based on discriminant analysis and mutual information. In order to obtain good classification performances, a selection of important features is done with a new developed algorithm based on the mutual information between variables. The application of the new fault diagnosis procedure on a benchmark problem, the Tennessee Eastman Process, shows better results than other well known published methods.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fault detection and identification with a new feature selection based on mutual information","attachmentId":72777667,"attachmentType":"pdf","work_url":"https://www.academia.edu/58295992/Fault_detection_and_identification_with_a_new_feature_selection_based_on_mutual_information","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/58295992/Fault_detection_and_identification_with_a_new_feature_selection_based_on_mutual_information"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="89020686" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89020686/Gearbox_faults_feature_selection_and_severity_classification_using_machine_learning">Gearbox faults feature selection and severity classification using machine learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6821220" href="https://independent.academia.edu/NinoslavZuber">Ninoslav Zuber</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Gearbox faults feature selection and severity classification using machine learning","attachmentId":92895144,"attachmentType":"pdf","work_url":"https://www.academia.edu/89020686/Gearbox_faults_feature_selection_and_severity_classification_using_machine_learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89020686/Gearbox_faults_feature_selection_and_severity_classification_using_machine_learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="89974771" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89974771/Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal">Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="243691960" href="https://independent.academia.edu/DiegoCabrera284">Diego Cabrera</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Sensors, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal","attachmentId":93666264,"attachmentType":"pdf","work_url":"https://www.academia.edu/89974771/Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89974771/Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="74259705" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/74259705/Article_Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal">Article Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="16801580" href="https://ula.academia.edu/MarielaCerrada">Mariela Cerrada</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2015</p><p class="ds-related-work--abstract ds2-5-body-sm">There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Article Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal","attachmentId":82472294,"attachmentType":"pdf","work_url":"https://www.academia.edu/74259705/Article_Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/74259705/Article_Multi_Stage_Feature_Selection_by_Using_Genetic_Algorithms_for_Fault_Diagnosis_in_Gearboxes_Based_on_Vibration_Signal"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="33359371" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/33359371/FAULT_DIAGNOSIS_FOR_WIND_TURBINE_BLADE_THROUGH_VIBRATION_SIGNALS_USING_STATISTICAL_FEATURES_AND_RANDOM_FOREST_ALGORITHM">FAULT DIAGNOSIS FOR WIND TURBINE BLADE THROUGH VIBRATION SIGNALS USING STATISTICAL FEATURES AND RANDOM FOREST ALGORITHM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="25854594" href="https://hindustanuniv.academia.edu/JoshuvaArockiaDhanraj">Joshuva Arockia Dhanraj</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017</p><p class="ds-related-work--abstract ds2-5-body-sm">Wind energy is one of the important renewable energy resources. The wind energy is converted into electrical energy using rotating blades which are connected to the generator. Due to environmental conditions and large structure, the blades are subjected to various faults and cause a lack of productivity. The downtime can be reduced when they are diagnosed periodically using structural health monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using random forest algorithm.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"FAULT DIAGNOSIS FOR WIND TURBINE BLADE THROUGH VIBRATION SIGNALS USING STATISTICAL FEATURES AND RANDOM FOREST ALGORITHM","attachmentId":53416920,"attachmentType":"pdf","work_url":"https://www.academia.edu/33359371/FAULT_DIAGNOSIS_FOR_WIND_TURBINE_BLADE_THROUGH_VIBRATION_SIGNALS_USING_STATISTICAL_FEATURES_AND_RANDOM_FOREST_ALGORITHM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/33359371/FAULT_DIAGNOSIS_FOR_WIND_TURBINE_BLADE_THROUGH_VIBRATION_SIGNALS_USING_STATISTICAL_FEATURES_AND_RANDOM_FOREST_ALGORITHM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="83915135" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83915135/A_New_Statistical_Features_Based_Approach_for_Bearing_Fault_Diagnosis_Using_Vibration_Signals">A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="71789942" href="https://independent.academia.edu/khanMuhammadAfzaal">Muhammad Afzaal khan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Sensors, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibratio...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals","attachmentId":89108816,"attachmentType":"pdf","work_url":"https://www.academia.edu/83915135/A_New_Statistical_Features_Based_Approach_for_Bearing_Fault_Diagnosis_Using_Vibration_Signals","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83915135/A_New_Statistical_Features_Based_Approach_for_Bearing_Fault_Diagnosis_Using_Vibration_Signals"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":101861009,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":101861009,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_101861009" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="15349617" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/15349617/Feature_selection_using_Decision_Tree_and_classification_through_Proximal_Support_Vector_Machine_for_fault_diagnostics_of_roller_bearing">Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34530657" 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href="https://uap-bd.academia.edu/RashedulIslam">Rashedul Islam</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Sensors, 2016</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors","attachmentId":71950893,"attachmentType":"pdf","work_url":"https://www.academia.edu/56688526/Discriminant_Feature_Distribution_Analysis_Based_Hybrid_Feature_Selection_for_Online_Bearing_Fault_Diagnosis_in_Induction_Motors","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" 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