Vehicle bearing fault diagnosis method based on CEEMDAN and APSO-SVM

Abstract

The invention discloses a vehicle bearing fault diagnosis method based on CEEMDAN and APSO-SVM, and the method comprises the steps: collecting a vibration signal of a rolling bearing, measuring the parameters of the bearing, carrying out the decomposition of the collected vibration signal of the rolling bearing, carrying out the screening of IMF modal components after the decomposition, carrying out the linear reconstruction of the screened components, and removing invalid information; then singular entropy, power spectrum entropy and energy entropy calculation is carried out on the screened IMF modal components, and principal feature extraction is carried out on the reconstructed signals by using WPCA based on calculation results to obtain feature vectors; making the feature vectors into a training set and a test set of an SVM, adding a category label, and constructing and optimizing an SVM classifier model on the basis; and finally, performing fault diagnosis on the vehicle bearing by using the optimized SVM classifier. According to the method, time domain and frequency domain information is comprehensively considered, fault features can be accurately extracted, the problem that the SVM optimal parameters are difficult to manually select is solved, popularization in engineering application is facilitated, and practicability is high.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a vehicle bearing fault diagnosis method based on CEEMDAN and APSO-SVM. Background technique [0002] Mechanical equipment such as trains and automobiles are developing in the direction of larger, more complex and more intelligent, and the functions they undertake are becoming more and more important. The probability of failure and the danger caused by failure are also becoming more and more serious. In these mechanical equipment Among them, rolling bearings are the most widely used parts and components with a high probability of failure. In order to ensure the safe and reliable operation of mechanical equipment at high speed, while reducing the cost of emergency maintenance and production, a bearing fault diagnosis technology that can identify quickly and diagnose accurately is very important. [0003] Scholars at home and abroad have done a lot of research on t...

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Problems solved by technology

[0004] The traditional PSO algorithm is a swarm intelligence optimization algorithm that simulates the foraging behavior of birds proposed by Eberhart et al. in 1995. The PSO algorithm has the advantages of simple principle, strong versatility, fewer parameters, and strong robustness. There are disadvantages that it is easy to fall into local optimum and cannot dynamically adjust the speed of particles, and it still needs to be improved in practical applications.

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