Yan, Bingxin, Sun, Qiuzhuang, Shen, Lijuan, and Ma, Xiaobing (2025) A Physical-Statistical Framework on Complex Mechanical System Fault Isolation. IEEE Transactions on Reliability, 74 (3). pp. 4091-4105.

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Abstract

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.

Item ID:	91177
Item Type:	Article (Research - C1)
ISSN:	1558-1721
Copyright Information:	© 2025 IEEE.
Date Deposited:	14 Apr 2026 00:10
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning @ 60% 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490508 Statistical data science @ 20% 40 ENGINEERING > 4017 Mechanical engineering > 401704 Mechanical engineering asset management @ 20%
SEO Codes:	27 TRANSPORT > 2703 Ground transport > 270306 Rail safety @ 60% 24 MANUFACTURING > 2412 Machinery and equipment > 241204 Industrial machinery and equipment @ 30% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 10%
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