| Title | Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Lal Senanayaka, Jagath Sri, Van Khang, Huynh, Robbersmyr, Kjell G. |
| Conference Name | 2018 XIII International Conference on Electrical Machines (ICEM) |
| Keywords | bearings, convolution, convolutional neural network, convolutional neural networks, cyber physical systems, Deep Learning, electric powertrains, electric vehicles, electrical fault detection, electromechanical systems, fault diagnosis, fault diagnosis methods, feedforward neural nets, Gears, human factors, Induction motors, learning (artificial intelligence), mechanical engineering computing, Mechanical power transmission, Metrics, multiple fault diagnosis, power availability, power engineering computing, power transmission (mechanical), pubcrawl, Resiliency, Spectrogram, Stator windings, system reliability, Training, variable speed operations, variable speeds, Vibrations |
| Abstract | 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. |
| DOI | 10.1109/ICELMACH.2018.8507096 |
| Citation Key | lal_senanayaka_multiple_2018 |
Multiple Fault Diagnosis of Electric Powertrains Under Variable Speeds Using Convolutional Neural Networks