A rolling bearing fault diagnosis method based on LCD de-noising and VPMCD

A fault diagnosis of rolling bearing based on LCD de-noising and VPMCD was proposed.Firstly, using the LCD on the rolling bearing vibration signals to reduce noise signals, then the fuzzy entropy of the de-noising signals in the different dimensions was calculated and as characteristic values. Using the VPMCD method to establish the fuzzy entropy prediction model, and finally the characteristic values of those unclassified signals samples were predicted by the model.The results of the prediction would be recognized by the model as accordance to classify. The experimental results prove that the LCD de-noising can effectively increase the VPMCD classification performance, compared with neural network and support vector machine classifier, the VPMCD methods can identify the work states and fault patterns of the rolling bearing more accurately and more effectively.