An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

Abstract

To process the non-stationary vibration signals and improve accuracy of bearing fault diagnosis, this paper presents a novel intelligent fault diagnosis method based on the adaptive fast ensemble empirical mode decomposition (AFEEMD) and one-dimensional convolutional neural networks (1D CNNs). First, the AFEEMD algorithm is utilized to decompose the raw signals into intrinsic mode functions (IMFs). Then, the time and frequency statistic features of the first several IMFs are analyzed to form feature vector, which are used as the input of 1D CNNs to identify the bearing fault. The performance of the proposed method is validated using the dataset from the Case Western Reserve University (CWRU). Compared with the traditional back propagation neural network (BPNN), the results show that the proposed AFEEMD-1D CNNs method not only can obtain higher accuracy and achieve more reliable performance, but also can improve the generalization performance. Due to the end-to-end feature learning capacity, it can be extended to other machinery for fault diagnosis.

The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

“…The VMD decomposition is deemed complete when the energy loss coefficient e reaches a set threshold ε. 30 When the number of VMD decompositions is determined, the calculation of the information entropy is introduced and the quadratic penalty factor is determined.…”