Bearing fault diagnosis based on self-adaptive impulse dictionary matching pursuit

A method of self-adaptive impulse dictionary matching pursuit for bearing fault diagnosis was presented here. Firstly, a new dictionary model based on the characteristics of bearing fault signals was established and bearing speed, and size etc. were introduced into the dictionary. According to the effect level of each key parameter in this model on the analysis results, the impact position information was taken as the first key parameter. A dictionary called the self-adaptive dictionary was built with the method of varying parameters individually. In this dictionary, each element was made to have a good similarity to the fault signals to be analyzed, so the dictionary's redundancy was reduced and its usage efficiency was lifted. Combining with the principle of matching pursuit, the method of self-adaptive impulse dictionary matching pursuit was established. At last, the results of simulations and tests with this proposed method showed that the bearing faults at different positions can be diagnosed effectively with the proposed method; this method is better than the method of matching pursuit with genetic algorithms.