Bearing Fault Diagnosis Based on AVMD and HPO-DBN

To address the issue of low identification rate in bearing failure diagnosis, this paper first extracts the temporal and spectral characteristics of the bearing vibration displacement signals. We propose an adaptive method called AVMD for extracting time-frequency domain characteristics from the bearing vibration displacement signals to the maximum extent possible. Next, the ReliefF algorithm is employed to select desired features, and an autoencoder is used to reduce the selected features dimensionally. Furthermore, because the Hunter-Prey Optimization (HPO) algorithm can balance multiple objectives during the search process by utilizing the concepts of hunter and prey to generate a better solution set. Incorporating this algorithm into the Deep Belief Network (DBN), we establish an HPO-DBN fault diagnosis model. Subsequently, we validate the proposed method using both public datasets and field compressor data. Moreover, we compare the results with those obtained from the Support Vector Machine (SVM). The findings indicate that this approach enhances the bearing fault identification rate, thus supporting predictive maintenance of bearings.