Abstract
This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The results demonstrate the algorithm we developed can detect anomalies which correspond to powertrain faults by learning patterns in the multivariate time-series data of hybrid-electric vehicle powertrain sensors. Data was collected by engineers at Ford Motor Company from numerous sensors over several drive cycle variations. This study provides evidence of the anomaly detecting capability of our trained autoencoder and investigates the suitability of our autoencoder relative to other unsupervised methods for automatic fault detection in this data set. Preliminary results of testing the autoencoder on the powertrain sensor data indicate the data reconstruction approach availed by the autoencoder is a robust technique for identifying the abnormal sequences in the multivariate series. These results support that irregularities in hybrid-electric vehicles' powertrains are conveyed via sensor signals in the embedded electronic communication system, and therefore can be identified mechanistically with a trained algorithm. Additional unsupervised methods are tested and show the autoencoder performs better at fault detection than outlier detectors and other novel deep learning techniques.
Original language | English (US) |
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Title of host publication | Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 |
Editors | Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 743-749 |
Number of pages | 7 |
ISBN (Electronic) | 9781665487689 |
DOIs | |
State | Published - 2022 |
Externally published | Yes |
Event | 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore Duration: Dec 4 2022 → Dec 7 2022 |
Publication series
Name | Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 |
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Conference
Conference | 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 |
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Country/Territory | Singapore |
City | Singapore |
Period | 12/4/22 → 12/7/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Anomaly detection
- automotive fault detection
- multivariate time series
- vehicle sensor analysis