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Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey
Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A. - In: Journal of Industrial Engineering and Management (JIEM) 15 (2022) 1, pp. 31-57
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D...
Persistent link: https://www.econbiz.de/10013326270
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Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks
Alblawi, Adel - In: Energy Reports 6 (2020), pp. 1083-1096
In the study presented in this paper, the deterioration in the performance of an industrial gas turbine during the operation design point was simulated by using the thermodynamic principle and a multi feedforward artificial neural networks (MFANN) system. Initially the thermodynamic model was...
Persistent link: https://www.econbiz.de/10012652465
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Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors : state of the art survey
Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A. - In: Journal of industrial engineering and management : JIEM 15 (2022) 1, pp. 31-57
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D...
Persistent link: https://www.econbiz.de/10012815091
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Fault Diagnosis on Medium Voltage (MV) Electric Power Distribution Networks: The Case of the Downstream Network of the AES-SONEL Ngousso Sub-Station
Tatietse, Thomas Tamo; Voufo, Joseph - In: Energies 2 (2009) 2, pp. 243-257
An analysis of the Medium Voltage (MV)electricity power distribution network operated by Cameroon’s AES-SONEL company shows that losses are very high due to energy which is produced but not distributed and that the duration of power interruptions as a result of these faults is long due to the...
Persistent link: https://www.econbiz.de/10010668107
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Canonical Correlation Between Partial Discharges and Gas Formation in Transformer Oil Paper Insulation
Chen, Weigen; Chen, Xi; Peng, Shangyi; Li, Jian - In: Energies 5 (2012) 4, pp. 1081-1097
Dissolved gas analysis (DGA) has been widely applied to diagnose internal faults in transformer insulation systems. However, the accuracy of DGA technique is limited because of the lack of positive correlation of the fault-identifying gases with faults found in power transformers. This paper...
Persistent link: https://www.econbiz.de/10010668181
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A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features
Ong, Pauline; John Koshy, Anelka; Lai Kee Huong; Chee … - In: Decision analytics journal 10 (2024), pp. 1-9
Gearbox failures can lead to substantial damage, significant financial losses due to maintenance downtimes, and, in some instances, fatalities. This study introduces an intelligent gear fault diagnosis system employing a convolutional neural network (CNN), utilizing vibration and thermal...
Persistent link: https://www.econbiz.de/10014541844
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A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains
Lahmiri, Salim - In: Supply chain analytics 2 (2023), pp. 1-7
Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various...
Persistent link: https://www.econbiz.de/10014516481
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A deep convolutional neural network for vibration-based health-monitoring of rotating machinery
Ong, Pauline; Tan Yean Keong; Lai Kee Huong; Chee Kiong Sia - In: Decision analytics journal 7 (2023), pp. 1-9
The gearbox is a critical component in the mechanical system, requiring vigilant monitoring to prevent adverse consequences on safety and quality due to malfunction. Therefore, early fault diagnosis of the gearbox before the fatal breakdown of the entire mechanical system is of imperative...
Persistent link: https://www.econbiz.de/10014436400
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Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks
Alblawi, Adel - In: Energy reports 6 (2020), pp. 1083-1096
In the study presented in this paper, the deterioration in the performance of an industrial gas turbine during the operation design point was simulated by using the thermodynamic principle and a multi feedforward artificial neural networks (MFANN) system. Initially the thermodynamic model was...
Persistent link: https://www.econbiz.de/10012220958
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Optimisation algorithms for the charge dispatch of plug-in vehicles based on variable tariffs
Link, Jochen; Büttner, Markus; Dallinger, David; … - 2010
grid impacts of variable tariffs for electric vehicles (BEV) as well as to schedule the charging process optimisation …. Therefore a combinatorial optimisation algorithm is developed and an algorithm based on graph search is used and customised … these optimisation algorithms, like the onboard dispatcher, which is used in the German fleet test "Flottenversuch …
Persistent link: https://www.econbiz.de/10010300726