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

Abstract

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.

A Comprehensive Survey on Arithmetic Optimization Algorithm

“…Through test experiments on CEC-BC-2017 benchmark functions, experimental findings demonstrate that the probability distribution-based AOA solves CEED problems superior than other algorithms, and the variable order penalty approach increases search efficiency and convergence speed compared to the fixed penalty approach. Influenced by various initialization techniques, Agushaka et al [93] presented an enhanced variant of AOA, namely nAOA, based on a probability distribution function.…”

“…The efficacy of presented method was evaluated using 30 benchmark functions and the result of this study demonstrates that use of beta distribution to generate initial population greatly enhance the algorithms performance compared to other tested algorithms. In order to examine how initialization methods affected the convergence rate and accuracy of the nAOA, the same author used 23 distinct PDFs with varying degrees of diversity in [93]. The study's findings demonstrated that the nAOA is susceptible to size of the population and iteration count, both of which must be high for best performance.…”