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

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 chal...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-06, Vol.24 (6), p.1-12
Main Authors: Wei, Zexian, He, Deqiang, Jin, Zhenzhen, Liu, Bin, Shan, Sheng, Chen, Yanjun, Miao, Jian
Format: Article
Language:English
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
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3253087