Broad learning and convolutional neural network aided principal component analysis for detecting faults in high-speed trains

Mingyue Zhou; Hongyang Zhao; Chao Cheng

doi:10.1117/12.2686568

10 August 2023 Broad learning and convolutional neural network aided principal component analysis for detecting faults in high-speed trains

Mingyue Zhou, Hongyang Zhao, Chao Cheng

Author Affiliations +

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592T (2023) https://doi.org/10.1117/12.2686568
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Traction systems provide the traction power of high-speed trains. Because the complex operation mechanism of train under actual working conditions and the measured data are nonlinear and non-Gaussian, and the sampling frequency of the sensor is high in actual working conditions. Directly using a neural network or the multivariate statistical method is challenging to obtain the ideal fault detection (FD) result. Therefore, this paper proposes a data-driven method based on broad learning system (BLS) and convolutional neural network (CNN) assisted principal component analysis (PCA). Two neural networks are used to enhance the robustness of the algorithm, so that the proposed method has better fault detection ability in nonlinear and non-Gaussian systems. The advantage of this method is that it does not require the establishment of a complex high-speed train data model. Instead, by processing the collected data, the proposed algorithm can ensure good fault detection capabilities. Finally, the effectiveness and feasibility of the proposed method are verified on the simulation platform of traction drive control system (TDCS).

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Mingyue Zhou, Hongyang Zhao, and Chao Cheng "Broad learning and convolutional neural network aided principal component analysis for detecting faults in high-speed trains", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592T (10 August 2023); https://doi.org/10.1117/12.2686568

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KEYWORDS

Neural networks

Principal component analysis

Convolutional neural networks

Detection and tracking algorithms

Mathematical modeling

Data processing

Sensors

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