Abstract: Since the mechanical fault diagnosis method based on federated learning lacks interpretability and is often regarded as a black box model, which reduces the trust of users to a certain extent. Based on this deficiency, a wavelet empowered federated learning (WEFL) model for mechanical fault diagnosis is prposed. In the proposed model, the wavelet coefficients are innovatively used to replace the local model parameters in traditional federated learning, and the wavelet coefficients are aggregated on the central server to reconstruct the global model. Due to the translation scale characteristics of wavelets, the constructed model greatly improves interpretability in federated learning. Simultaneously, local model parameters are replaced with wavelet coefficients, greatly reduce the computational cost of parameters and significantly accelerates the convergence. The experimenta test verify the effectiveness of the proposed model. Compared with the traditional federated learning model, the WEFL model has more transparency and interpretability in the decision-making process of mechanical fault diagnosis. Compared with the wavelet deep learning network (e.g. WaveletKernelNet), the WEFL model can effectively protect data privacy and solve the problem of data island.

Key words: fault diagnosis, federated learning, interpretability, wavelet decomposition