Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 6; pp. 1 - 12
• Main Authors: Wei, Zexian, He, Deqiang, Jin, Zhenzhen, Liu, Bin, Shan, Sheng, Chen, Yanjun, Miao, Jian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affinity; Algorithms; Bogie-bearing; Clustering; Clustering algorithms; Density; Fault detection; Fault diagnosis; Frequency variation; intelligent fault diagnosis; Load fluctuation; Mathematical analysis; Monitoring; Propagation; Rail transportation; Rails; Signal processing algorithms; Subways; tensor clustering; Tensors; Time complexity; Undercarriages; variable working conditions; Working conditions
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3253087

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.