Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 17
• Main Authors: Liu, Shuli, Liu, Yi, Chang, Qi, Li, Lin, Man, Junfeng, Liu, Kai, Shen, Yiping, Luo, Jiaxin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 16.12.2022; Hindawi Limited
• Subjects: Algorithms; Arithmetic; Bearings; Cracks; Data collection; Fault diagnosis; Feature extraction; Feature selection; Fractals; Machine learning; Machinery; Mathematical problems; Methods; Neural networks; Optimization; Parameter modification; Parameter sensitivity; Pattern recognition; Principal components analysis; Sensors; Signal processing; Support vector machines; Traction; Undercarriages; Urban rail; Vehicles
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/1221186

The bogie traction seat is the main part of urban rail vehicles and its fault status will affect the safe and smooth operation of the vehicles. For the low accuracy of the traditional detection methods, an intelligent fault diagnosis model of the traction seat based on principal component analysis with one versus one (PCA-OVO) and support vector machine (SVM) optimized by modified arithmetic optimization algorithm is proposed. Firstly, for the difficulty of high-frequency data collection under real working conditions, the simulation platform of the bogie of an urban rail vehicle is built, and the vibration signals of the traction seat are collected and processed in different domains, and then the feature extraction and fusion method based on PCA-OVO is used to obtain the sensitive feature set of the traction seat. Finally, the SVM intelligence recognition model is constructed based on the sensitive feature set data, and its parameters are optimally combined and selected by the modified arithmetic optimization algorithm after introducing the cosine factor. The experiments prove the effectiveness of the model. Experimental results show that the model is effective and provides a new model for fault diagnosis of traction seat of urban rail vehicles.