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RESEARCH PRODUCT
Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks
Kjell G. RobbersmyrJagath Sri Lal SenanayakaHuynh Van Khangsubject
0209 industrial biotechnologyComputer sciencebusiness.industryPowertrainStatorDeep learningReliability (computer networking)020208 electrical & electronic engineeringControl engineeringHardware_PERFORMANCEANDRELIABILITY02 engineering and technologyFault (power engineering)Convolutional neural networklaw.inventionPower (physics)020901 industrial engineering & automationlaw0202 electrical engineering electronic engineering information engineeringArtificial intelligencebusinessInduction motordescription
Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-09-01 | 2018 XIII International Conference on Electrical Machines (ICEM) |