Abstract
By dealing with the non-Gaussian measurement and slow-change faults in running gear systems, this paper presents a fault detection (FD) scheme named time-series independent component analysis (TsICA), where the time-series characteristic is taken into account. Time-series algorithms can extract slow-change information in the data. The advantages of the proposed method are: 1) it can improve the FD power; 2) it considers the information in the data 3) it is suitable for non-Gaussian systems; 4) it is sensitive to slow-change faults; 5) it can effectively shorten the first time of fault detection. The feasibility of the proposed scheme is verified through a case study on running gear systems.
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This work is supported by the National Natural Science Foundation of China (61903047, U20A20186), Jilin Science and Technology Department (20200401127GX).
Chao Cheng received his Ph.D. degree from Jilin University, Changchun, China, in 2014. He is currently a Teacher with the Changchun University of Technology, Changchun. He has been a Post-Doctoral Fellow in process control engineering with the Department of Automation, Tsinghua University, Beijing, China, since 2018. He has also been a Post-Doctoral Fellow with the National Engineering Laboratory, CRRC Changchun Railway Vehicles Co., Ltd., China, since 2018. His research interests include dynamic system fault diagnosis and predictive maintenance, wireless sensor network, artificial intelligence, and data-driven method.
Sheng Yang received his B.Eng. degree from Wuhan East Lake University, Wuhan, China, in 2014. He is currently working toward an M.Eng. degree in computer science and engineering with the Changchun University of Technology. His research interests include complex system fault diagnosis, health status estimation, and data-driven fault detection and diagnosis.
Yu Song received his B.S. degree from Changchun University of technology, and received an M.S. degree from Shenyang University of technology. He currently works in Changchun University of technology. He is mainly engaged in fault detection, embedded systems, and UAVs.
Gang Liu is a teacher at Changchun University of Technology. He mainly teaches courses such as Introduction to Computer Subjects; Software Requirements; Software Design Patterns and Software Architecture. He is mainly engaged in data mining, distributed systems, and software engineering.
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Cheng, C., Yang, S., Song, Y. et al. Time-series Independent Component Analysis-aided Fault Detection for Running Gear Systems. Int. J. Control Autom. Syst. 20, 2892–2901 (2022). https://doi.org/10.1007/s12555-021-0276-9
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DOI: https://doi.org/10.1007/s12555-021-0276-9