||Wavelet Transform-based Identification of Vibration Fault Signals in Rotating Machinery
|| Rotating machinery; Vibration signals; Fault identification; Capsule networks; Frequency-slicing wavelet transform
||The study of fault identification of vibration signals from rotating machinery is essential for enhancing industrial production safety. A method combining a capsule network and frequency-slicing wavelet transform is proposed to improve the fault identification accuracy, considering the problem that the original vibration signal of rotating machinery carries multiple noises. The capsule network learning model was also optimized using a dynamic weighting method based on the channel attention mechanism, considering the variable operating conditions of rotating machinery. The dynamic weighting algorithm based on the channel attention mechanism used in the study achieved the highest fault recognition rates, with 99.65%, 99.25%, and 99.90% on sensor 1, sensor 2, and feature fusion data, respectively. Hence, the proposed model for fault identification in rotating machinery vibration signals is superior to other models.