Rolling bearing fault diagnosis based on wavelet packet decomposition and PSO-BPN

In view of problems in existing rolling bearing fault diagnosis methods for coal mine rotating machinery, such as incomplete signal feature extraction, low fault diagnosis accuracy and low efficiency, a rolling bearing fault diagnosis method based on wavelet packet decomposition and particle swarm optimization BP neural network was proposed. The method includes signal feature extraction and fault type recognition. In the signal feature extraction part, the collected vibration signals of rolling bearing are decomposed by wavelet packet to obtain energy of each sub-frequency band and total energy of the signal. After normalization processing, feature vector representing state of rolling bearing is obtained. In the fault type recognition part, initial weight and threshold of BP neural network are optimized by particle swarm optimization to accelerate convergence speed of the network and avoid falling into local minimum. The experimental results show that the method improves fault diagnosis efficiency and accuracy of rolling bearing.