Fault diagnosis method for rolling element bearing with variable rotating speed using envelope order spectrum and convolutional neural network

Features of raw bearing vibration signals aren't invariant with the change of rotating speed. As a result, determining the proper features is essential for the feature learning based intelligent fault diagnosis method for rolling element bearing with varying rotating speed. To address this issue, a convolutional neural network (CNN) based fault diagnosis approach is proposed. In the proposed method, envelope order spectra extracted from the raw vibration signals are used to provide abundant information about the fault characteristic orders, which are features invariant to the rotating speed. Subsequently, to extract these representative features automatically, a CNN model is constructed and employed, which avoid the manual feature selection. Finally, the type of bearing defects can be recognized successfully. In the experimental verification, the CNN is trained using a data set corresponds to one revolution per minute (RPM), while the data sets correspond to other RPMs are employed to verify the classification accuracy of the trained CNN, which can reflect the effectiveness of proposed method for bearing fault detection under different rotating speed. Experimental results show the satisfactory performance of fault-pattern recognition for the proposed method. When compared with some other approaches using intelligence-based fault diagnosis method, the results show the superiority of the proposed method.