Fault diagnosis of rolling bearing based on wavelet threshold noise reduction EMD-AR spectrum analysis and extreme learning machine

In the traditional rolling bearing fault diagnosis, vibration characteristics are easy to be interfered by redundant noise, and fault characteristics can not be accurately classified, a rolling bearing fault diagnosis method based on wavelet denoising, EMD-AR spectrum analysis and ELM (extreme learning Machine) was proposed. The vibration signals of rolling bearings are firstly de-noised by wavelet threshold, then the de-noised one-dimensional signals are decomposed by EMD and the first six IMF components are extracted. The AR spectra of the first six IMF components are accumulated to obtain the EMD-AR spectra of the de-noised vibration signals, from which different fault conditions of bearings can be seen as a prior diagnosis. Finally, six eigenvalues of the denoised signal were extracted as samples. In order to avoid the contingency of the experiment, the ELM classification diagnosis model based on K-fold cross-validation was established. The diagnosis results show that the method can clearly classify the bearing fault situation, the classification accuracy is up to 100%, and can provide a new method for the bearing fault diagnosis.