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ignition engine</title> <meta name="description" content="Search results for: spark ignition engine"> <meta name="keywords" content="spark ignition engine"> <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 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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="spark ignition engine"> <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> 966</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: spark ignition engine</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Sampling and Chemical Characterization of Particulate Matter in a Platinum Mine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juergen%20Orasche">Juergen Orasche</a>, <a href="https://publications.waset.org/abstracts/search?q=Vesta%20Kohlmeier"> Vesta Kohlmeier</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20C.%20Dragan"> George C. Dragan</a>, <a href="https://publications.waset.org/abstracts/search?q=Gert%20Jakobi"> Gert Jakobi</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20Forbes"> Patricia Forbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Ralf%20Zimmermann"> Ralf Zimmermann</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Underground mining poses a difficult environment for both man and machines. At more than 1000 meters underneath the surface of the earth, ores and other mineral resources are still gained by conventional and motorised mining. Adding to the hazards caused by blasting and stone-chipping, the working conditions are best described by the high temperatures of 35-40°C and high humidity, at low air exchange rates. Separate ventilation shafts lead fresh air into a mine and others lead expended air back to the surface. This is essential for humans and machines working deep underground. Nevertheless, mines are widely ramified. Thus the air flow rate at the far end of a tunnel is sensed to be close to zero. In recent years, conventional mining was supplemented by mining with heavy diesel machines. These very flat machines called Load Haul Dump (LHD) vehicles accelerate and ease work in areas favourable for heavy machines. On the other hand, they emit non-filtered diesel exhaust, which constitutes an occupational hazard for the miners. Combined with a low air exchange, high humidity and inorganic dust from the mining it leads to 'black smog' underneath the earth. This work focuses on the air quality in mines employing LHDs. Therefore we performed personal sampling (samplers worn by miners during their work), stationary sampling and aethalometer (Microaeth MA200, Aethlabs) measurements in a platinum mine in around 1000 meters under the earth’s surface. We compared areas of high diesel exhaust emission with areas of conventional mining where no diesel machines were operated. For a better assessment of health risks caused by air pollution we applied a separated gas-/particle-sampling tool (or system), with first denuder section collecting intermediate VOCs. These multi-channel silicone rubber denuders are able to trap IVOCs while allowing particles ranged from 10 nm to 1 µm in diameter to be transmitted with an efficiency of nearly 100%. The second section is represented by a quartz fibre filter collecting particles and adsorbed semi-volatile organic compounds (SVOC). The third part is a graphitized carbon black adsorber – collecting the SVOCs that evaporate from the filter. The compounds collected on these three sections were analyzed in our labs with different thermal desorption techniques coupled with gas chromatography and mass spectrometry (GC-MS). VOCs and IVOCs were measured with a Shimadzu Thermal Desorption Unit (TD20, Shimadzu, Japan) coupled to a GCMS-System QP 2010 Ultra with a quadrupole mass spectrometer (Shimadzu). The GC was equipped with a 30m, BP-20 wax column (0.25mm ID, 0.25µm film) from SGE (Australia). Filters were analyzed with In-situ derivatization thermal desorption gas chromatography time-of-flight-mass spectrometry (IDTD-GC-TOF-MS). The IDTD unit is a modified GL sciences Optic 3 system (GL Sciences, Netherlands). The results showed black carbon concentrations measured with the portable aethalometers up to several mg per m³. The organic chemistry was dominated by very high concentrations of alkanes. Typical diesel engine exhaust markers like alkylated polycyclic aromatic hydrocarbons were detected as well as typical lubrication oil markers like hopanes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diesel%20emission" title="diesel emission">diesel emission</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20sampling" title=" personal sampling"> personal sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=aethalometer" title=" aethalometer"> aethalometer</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a> </p> <a href="https://publications.waset.org/abstracts/86968/sampling-and-chemical-characterization-of-particulate-matter-in-a-platinum-mine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86968.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">157</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">5</span> Risks for Cyanobacteria Harmful Algal Blooms in Georgia Piedmont Waterbodies Due to Land Management and Climate Interactions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sam%20Weber">Sam Weber</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Mishra"> Deepak Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Wilde"> Susan Wilde</a>, <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Kramer"> Elizabeth Kramer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyanobacteria" title="cyanobacteria">cyanobacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%2Fland%20cover" title=" land use/land cover"> land use/land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20mapping" title=" risk mapping"> risk mapping</a> </p> <a href="https://publications.waset.org/abstracts/79007/risks-for-cyanobacteria-harmful-algal-blooms-in-georgia-piedmont-waterbodies-due-to-land-management-and-climate-interactions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79007.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">211</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">4</span> Recrystallization Behavior and Microstructural Evolution of Nickel Base Superalloy AD730 Billet during Hot Forging at Subsolvus Temperatures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Perez">Marcos Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20Dumont"> Christian Dumont</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Nodin"> Olivier Nodin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastien%20Nouveau"> Sebastien Nouveau</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nickel superalloys are used to manufacture high-temperature rotary engine parts such as high-pressure disks in gas turbine engines. High strength at high operating temperatures is required due to the levels of stress and heat the disk must withstand. Therefore it is necessary parts made from materials that can maintain mechanical strength at high temperatures whilst remain comparatively low in cost. A manufacturing process referred to as the triple melt process has made the production of cast and wrought (C&W) nickel superalloys possible. This means that the balance of cost and performance at high temperature may be optimized. AD730TM is a newly developed Ni-based superalloy for turbine disk applications, with reported superior service properties around 700°C when compared to Inconel 718 and several other alloys. The cast ingot is converted into billet during either cogging process or open die forging. The semi-finished billet is then further processed into its final geometry by forging, heat treating, and machining. Conventional ingot-to-billet conversion is an expensive and complex operation, requiring a significant amount of steps to break up the coarse as-cast structure and interdendritic regions. Due to the size of conventional ingots, it is difficult to achieve a uniformly high level of strain for recrystallization, resulting in non-recrystallized regions that retain large unrecrystallized grains. Non-uniform grain distributions will also affect the ultrasonic inspectability response, which is used to find defects in the final component. The main aim is to analyze the recrystallization behavior and microstructural evolution of AD730 at subsolvus temperatures from a semi-finished product (billet) under conditions representative of both cogging and hot forging operations. Special attention to the presence of large unrecrystallized grains was paid. Double truncated cones (DTCs) were hot forged at subsolvus temperatures in hydraulic press, followed by air cooling. SEM and EBSD analysis were conducted in the as-received (billet) and the as-forged conditions. AD730 from billet alloy presents a complex microstructure characterized by a mixture of several constituents. Large unrecrystallized grains present a substructure characterized by large misorientation gradients with the formation of medium to high angle boundaries in their interior, especially close to the grain boundaries, denoting inhomogeneous strain distribution. A fine distribution of intragranular precipitates was found in their interior, playing a key role on strain distribution and subsequent recrystallization behaviour during hot forging. Continuous dynamic recrystallization (CDRX) mechanism was found to be operating in the large unrecrystallized grains, promoting the formation intragranular DRX grains and the gradual recrystallization of these grains. Evidences that hetero-epitaxial recrystallization mechanism is operating in AD730 billet material were found. Coherent γ-shells around primary γ’ precipitates were found. However, no significant contribution to the overall recrystallization during hot forging was found. By contrast, strain presents the strongest effect on the microstructural evolution of AD730, increasing the recrystallization fraction and refining the structure. Regions with low level of deformation (ε ≤ 0.6) were translated into large fractions of unrecrystallized structures (strain accumulation). The presence of undissolved secondary γ’ precipitates (pinning effect), prior to hot forging operations, could explain these results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AD730%20alloy" title="AD730 alloy">AD730 alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20dynamic%20recrystallization" title=" continuous dynamic recrystallization"> continuous dynamic recrystallization</a>, <a href="https://publications.waset.org/abstracts/search?q=hot%20forging" title=" hot forging"> hot forging</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%B3%E2%80%99%20precipitates" title=" γ’ precipitates"> γ’ precipitates</a> </p> <a href="https://publications.waset.org/abstracts/98142/recrystallization-behavior-and-microstructural-evolution-of-nickel-base-superalloy-ad730-billet-during-hot-forging-at-subsolvus-temperatures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98142.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">3</span> Location3: A Location Scouting Platform for the Support of Film and Multimedia Industries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Tzilopoulos">Dimitrios Tzilopoulos</a>, <a href="https://publications.waset.org/abstracts/search?q=Panagiotis%20Symeonidis"> Panagiotis Symeonidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Loufakis"> Michael Loufakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimosthenis%20Ioannidis"> Dimosthenis Ioannidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Tzovaras"> Dimitrios Tzovaras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The domestic film industry in Greece has traditionally relied heavily on state support. While film productions are crucial for the country's economy, it has not fully capitalized on attracting and promoting foreign productions. The lack of motivation, organized state support for attraction and licensing, and the absence of location scouting have hindered its potential. Although recent legislative changes have addressed the first two of these issues, the development of a comprehensive location database and a search engine that would effectively support location scouting at the pre-production location scouting is still in its early stages. In addition to the expected benefits of the film, television, marketing, and multimedia industries, a location-scouting service platform has the potential to yield significant financial gains locally and nationally. By promoting featured places like cultural and archaeological sites, natural monuments, and attraction points for visitors, it plays a vital role in both cultural promotion and facilitating tourism development. This study introduces LOCATION3, an internet platform revolutionizing film production location management. It interconnects location providers, film crews, and multimedia stakeholders, offering a comprehensive environment for seamless collaboration. The platform's central geodatabase (PostgreSQL) stores each location’s attributes, while web technologies like HTML, JavaScript, CSS, React.js, and Redux power the user-friendly interface. Advanced functionalities, utilizing deep learning models, developed in Python, are integrated via Node.js. Visual data presentation is achieved using the JS Leaflet library, delivering an interactive map experience. LOCATION3 sets a new standard, offering a range of essential features to enhance the management of film production locations. Firstly, it empowers users to effortlessly upload audiovisual material enriched with geospatial and temporal data, such as location coordinates, photographs, videos, 360-degree panoramas, and 3D location models. With the help of cutting-edge deep learning algorithms, the application automatically tags these materials, while users can also manually tag them. Moreover, the application allows users to record locations directly through its user-friendly mobile application. Users can then embark on seamless location searches, employing spatial or descriptive criteria. This intelligent search functionality considers a combination of relevant tags, dominant colors, architectural characteristics, emotional associations, and unique location traits. One of the application's standout features is the ability to explore locations by their visual similarity to other materials, facilitated by a reverse image search. Also, the interactive map serves as both a dynamic display for locations and a versatile filter, adapting to the user's preferences and effortlessly enhancing location searches. To further streamline the process, the application facilitates the creation of location lightboxes, enabling users to efficiently organize and share their content via email. Going above and beyond location management, the platform also provides invaluable liaison, matchmaking, and online marketplace services. This powerful functionality bridges the gap between visual and three-dimensional geospatial material providers, local agencies, film companies, production companies, etc. so that those interested in a specific location can access additional material beyond what is stored on the platform, as well as access production services supporting the functioning and completion of productions in a location (equipment provision, transportation, catering, accommodation, etc.). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20models" title="deep learning models">deep learning models</a>, <a href="https://publications.waset.org/abstracts/search?q=film%20industry" title=" film industry"> film industry</a>, <a href="https://publications.waset.org/abstracts/search?q=geospatial%20data%20management" title=" geospatial data management"> geospatial data management</a>, <a href="https://publications.waset.org/abstracts/search?q=location%20scouting" title=" location scouting"> location scouting</a> </p> <a href="https://publications.waset.org/abstracts/169034/location3-a-location-scouting-platform-for-the-support-of-film-and-multimedia-industries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169034.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">71</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">2</span> Developing a Cloud Intelligence-Based Energy Management Architecture Facilitated with Embedded Edge Analytics for Energy Conservation in Demand-Side Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu-Hsiu%20Lin">Yu-Hsiu Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen-Chun%20Lin"> Wen-Chun Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yen-Chang%20Cheng"> Yen-Chang Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Ju%20Yeh"> Chia-Ju Yeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Chuan%20Chen"> Yu-Chuan Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-You%20Li"> Tai-You Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand-side%20management" title="demand-side management">demand-side management</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20intelligence" title=" edge intelligence"> edge intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management%20system" title=" energy management system"> energy management system</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20classification" title=" fault detection and classification"> fault detection and classification</a> </p> <a href="https://publications.waset.org/abstracts/85791/developing-a-cloud-intelligence-based-energy-management-architecture-facilitated-with-embedded-edge-analytics-for-energy-conservation-in-demand-side-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85791.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">251</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">1</span> XAI Implemented Prognostic Framework: Condition Monitoring and Alert System Based on RUL and Sensory Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faruk%20Ozdemir">Faruk Ozdemir</a>, <a href="https://publications.waset.org/abstracts/search?q=Roy%20Kalawsky"> Roy Kalawsky</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Hubbard"> Peter Hubbard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title="predictive maintenance">predictive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=explainable%20artificial%20intelligence" title=" explainable artificial intelligence"> explainable artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=prognostic" title=" prognostic"> prognostic</a>, <a href="https://publications.waset.org/abstracts/search?q=RUL" title=" RUL"> RUL</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=turbofan%20engines" title=" turbofan engines"> turbofan engines</a>, <a href="https://publications.waset.org/abstracts/search?q=C-MAPSS%20dataset" title=" C-MAPSS dataset"> C-MAPSS dataset</a> </p> <a href="https://publications.waset.org/abstracts/194369/xai-implemented-prognostic-framework-condition-monitoring-and-alert-system-based-on-rul-and-sensory-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194369.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">6</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=spark%20ignition%20engine&page=32" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" 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