Multiple Convolutional Recurrent Neural Networks for Fault Identification and Performance Degradation Evaluation of High-Speed Train Bogie

As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identif...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 31; no. 12; pp. 5363 - 5376
Main Authors: Qin, Na, Liang, Kaiwei, Huang, Deqing, Ma, Lei, Kemp, Andrew H.
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bogie
convolutional recurrent neural network (CRNN)
Deep learning
Degradation
Fault diagnosis
Feature extraction
High speed rail
High-speed rail transportation
high-speed train (HST)
Locomotives
Maintenance
Mechanical properties
Neural networks
Performance degradation
Performance evaluation
Recurrent neural networks
Undercarriages
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.2966744