- Title
- Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles
- Creator
- Rezaei, Mohammad; Fathollahi, Arman; Rezaei, Sajad; Hu, Jiefeng; Gheisarnejad, Meysam; Teimouri, Ali; Rituraj, Rituraj; Mosavi, Amir; Khooban, Mohammad-Hassan
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190467
- Identifier
- vital:17639
- Identifier
-
https://doi.org/10.1109/ACCESS.2022.3228916
- Identifier
- ISSN:2169-3536 (ISSN)
- Abstract
- The DC-Link capacitor is defined as the essential electronics element which sources or sinks the respective currents. The reliability of DC-link capacitor-banks (CBs) encounters many challenges due to their usage in electric vehicles. Heavy shocks may damage the internal capacitors without shutting down the CB. The fundamental development obstacles of CBs are: lack of considering capacitor degradation in reliability assessment, the impact of unforeseen sudden internal capacitor faults in forecasting CB lifetime, and the faults consequence on CB degradation. The sudden faults change the CB capacitance, which leads to reliability change. To more accurately estimate the reliability, the type of the fault needs to be detected for predicting the correct post-fault capacitance. To address these practical problems, a new CB model and reliability assessment formula covering all fault types are first presented, then, a new analog fault-detection method is presented, and a combination of online-learning long short-term memory (LSTM) and fault-detection method is subsequently performed, which adapt the sudden internal CB faults with the LSTM to correctly predict the CB degradation. To confirm the correct LSTM operation, four capacitors degradation is practically recorded for 2000-hours, and the off-line faultless degradation values predicted by the LSTM are compared with the actual data. The experimental findings validate the applicability of the proposed method. The codes and data are provided. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 10, no. (2022), p. 132271-132287
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ 2022 IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; Artificial intelligence (AI); Capacitor-bank; Deep learning; Electronics; Machine learning; Power system reliability
- Full Text
- Reviewed
- Funder
- This work was supported by the European Union Horizon 2020 Research and Innovation Programme under the Programme SASPRO 2 COFUND Marie Sklodowska-Curie grant agreement No. 945478. In addition, DFKI is supported by The Lower Saxony Ministry of Science and Culture.
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