A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

Chao Cheng, Weijun Wang (), He Di, Xuedong Li, Haotong Lv and Zhiwei Wan
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Chao Cheng: Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, China
Weijun Wang: Department of Mathematics and Statistics, Changchun University of Technology, Changchun 130000, China
He Di: Department of Communication Engineering, Jilin University, Changchun 130000, China
Xuedong Li: Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, China
Haotong Lv: Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, China
Zhiwei Wan: Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130000, China

Mathematics, 2024, vol. 12, issue 20, 1-16

Abstract: The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

Keywords: fault detection; fault estimation; subspace identification; electrical systems; Kalman filter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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