Fault diagnosis of rolling bearings based on acoustic signals

Here, combining wavelet packet short-term energy dispersion entropy (STE-DE), backtracking search algorithm (BSA) and learning vector quantization (LVQ) neural network, a new method for rolling bearing fault diagnosis based on acoustic signals was proposed. Firstly, wavelet packet decomposition was combined with STE to extract pulse energy of acoustic signals, highlight energy distribution of time-frequency subspace correlated to faults, and then feature matrix was constructed by calculating DE of STE sequence of each subspace. The t-distribution random neighborhood embedding method was used to perform dimensionality reduction clustering for the extracted features. It was shown that the extracted features have better clustering performance. Then, BSA was used to optimize LVQ, and establish a neural network fault diagnosis model. The established model was used to identify bearing faults, and it was compared with various diagnostic methods. The experimental results showed that after adding STE-DE, this model can improve energy characteristics of acoustic signals, optimize the feature matrix, and have the optimal diagnostic performance. © 2023 Chinese Vibration Engineering Society. All rights reserved.