Journal
CONTROL ENGINEERING PRACTICE
Volume 80, Issue -, Pages 146-156Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2018.08.013
Keywords
Fault diagnosis; Fault isolation; Machine learning; Artificial intelligence; Classification
Funding
- Volvo Car Corporation in Gothenburg, Sweden
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Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine.
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