An intelligent diagnosis method of switch machine based on deep belief network

doi:10.11835/j.issn.1000-582X.2023.07.008

Home > Archive>Volume 46, Issue 7, 2023 >75-85. DOI:10.11835/j.issn.1000-582X.2023.07.008

An intelligent diagnosis method of switch machine based on deep belief network
DOI:
                        10.11835/j.issn.1000-582X.2023.07.008
                    
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                        SI Yongbo1,2SI Yongbo
Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, P. R. China
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ZHANG Guorui1,2ZHANG Guorui
Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, P. R. China
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CHEN Guangwu1,2CHEN Guangwu
Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, P. R. China
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WEI Zongshou1,2WEI Zongshou
Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, P. R. China
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Affiliation:1.Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;2.Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, P. R. China
Clc Number:U284
Fund Project:Supported by National Natural Science Foundation of China(61863024, 71761023), the Scientific Research Funds for Colleges and Universities in Gansu Province(2018C-11, 2018A-22), and Natural Science Foundation of Gansu Province(18JR3RA130).

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Abstract:

The traditional fault diagnosis method often relies on the complex signal processing procedures and experts’ rich experience. It requires precise signal segmentation, which is a tedious process and is not conducive to the field use. In this paper, the deep belief network (DBN) method optimized by particle swarm optimization (PSO) is used to directly extract features from the original power data, and the restricted Boltzmann machine (RBM) is employed to fit the data features layer by layer, achieving the data dimension reduction at the same time. Then, extreme learning machine (ELM) is used to classify each state, thereby improving the diagnosis speed. The results show the accuracy reaches 96%, which is a 4% improvement, and the required time is significantly reduced, when compared to support vector machine (SVM) optimized by PSO.

Key words:S700K switch machine;DBN;feature extraction;ELM;fault diagnosis

Reference

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司涌波,张国瑞,陈光武,魏宗寿.基于深度置信网络的道岔故障智能诊断方法[J].重庆大学学报,2023,46(7):75~85

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Received:August 11,2020
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Online: August 02,2023
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