Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

SUN Yongkui; CAO Yuan; XIE Guo; WEN Tao

doi:10.1049/cje.2020.06.007

Volume 29 Issue 4

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SUN Yongkui, CAO Yuan, XIE Guo, et al., “Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 786-792, 2020, doi: 10.1049/cje.2020.06.007

Citation:

SUN Yongkui, CAO Yuan, XIE Guo, et al., “Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine,” Chinese Journal of Electronics, vol. 29, no. 4, pp. 786-792, 2020, doi: 10.1049/cje.2020.06.007

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Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

doi: 10.1049/cje.2020.06.007

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
2.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China;
3.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Shaanxi 710048, China

Funds: This work is supported by the Fundamental Research Funds for the Central Universities (No.2018YJS021), and the National Natural Science Foundation of China (No.U1734211, No.U1534208).

Received Date: 2019-06-28
Rev Recd Date: 2019-08-29
Publish Date: 2020-07-10

Abstract

Abstract

Railway point machines (RPMS) are one of the key equipments in the railway system to switch different routes for the trains. Condition monitoring for RPMs is a vital measure to keep train operation safe and reliable. Taking convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is proposed. Time-domain and frequency-domain features are obtained, and normalized using z-score standardization method to eliminate the influences of different dimensions. Binary particle swarm optimization (BPSO) is utilized to select the most significant discrimination feature subset. The effects of the selected optimal features are verified using Support vector machine (SVM), 1-Nearest neighbor (1NN), Random forest (RF), and Naive Bayes (NB). Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three classifiers. The identification accuracies on normal switching and reverse switching processes reach 100% and 99.67%, respectively, indicating the feasibility of the proposed method.
- Condition monitoring,
- Sound analysis,
- Railway point machines (RPMs),
- Binary particle swarm optimization (BPSO),
- Support vector machine (SVM)

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References(42)

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