A Physical-Statistical Framework on Complex Mechanical System Fault Isolation
Journal Publication ResearchOnline@JCUAbstract
Supervisory control and data acquisition (SCADA) data from a complex mechanical system, such as a high-speed train power bogie, nonpower bogie, and wind turbine, are widely used for anomaly detection and fault isolation. The SCADA data include measurements of process variables and exogenous covariates for key components in the system. The process variables refer to the performance characteristics of the key component while the exogenous covariates are working loads or working conditions of the complex mechanical system. Dominated by such physical mechanisms as dynamic motion laws of the system, there are complex relationships between the process variables and covariates, that complicate anomaly detection and fault isolation. To solve this problem, we propose a framework that integrates physical knowledge and statistical learning. We first build a spline model to capture the relationship between process variables and exogenous covariates. To make the model interpretable, we use physical knowledge to impose constraints on the model parameters. We then conduct anomaly detection at a system level based on the physical-statistical regression model. Once an anomaly is detected, we propose a Lasso-based method to isolate the faulty components. Our fault isolation method does not require historical failure data or knowing the true number of faulty components. Real-world case studies on power bogies from high-speed trains illustrate the advantages of our framework: the best benchmark achieves at least 2.50% lower F1-score in anomaly detection and 6.01% lower F1-score in fault isolation compared to our method.
Journal
IEEE Transactions on Reliability
Publication Name
IEEE Transactions on Reliability
Volume
74
ISBN/ISSN
1558-1721
Edition
N/A
Issue
3
Pages Count
15
Location
N/A
Publisher
Institute of Electrical and Electronics Engineers
Publisher Url
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Publisher Location
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Publish Date
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Url
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Date
N/A
EISSN
N/A
DOI
10.1109/TR.2025.3549216