A Variable Working Condition Rolling Bearing Fault Diagnosis Method Based on Improved Triplet Loss Algorithm

In the case of large differences in working conditions and large noise effects, how to maintain the similarity of the same fault type is a major difficulty in the rolling bearing fault diagnosis. In addition, variable working conditions will cause the signal take place the local modulation, which makes the signal more susceptible to noise. Most of classification algorithms are difficult to eliminate the influence of variable working conditions on diagnostic results. To solve the problem, a fault diagnosis method is proposed which takes into account the change of speed and load in this paper. The method first applies the synchronous compression wavelet transformation to pre-process the data to reduce the effect of noise on feature extraction. Then, the Triplet loss function is improved and combined with the Hard Samples Mining theory to proposes a new loss function called Quadru-Hard loss to solve the problem of difficult classification under variable working conditions. Based on the experimental analysis of two sets of bearing fault data under variable speeds and variable loads, the results show that the method has a highly accuracy in fault diagnosis under two variable conditions.