Research on federated learning method for fault diagnosis in multiple working conditions

Abstract

As one of the critical components of rotating machinery, fault diagnosis of rolling bearings has great significance. Although deep learning is useful in diagnosing rolling bearing faults, it is difficult to diagnose the faults of bearings under multiple operating conditions. To overcome the above-mentioned problem, this paper designs a modular federated learning network for fault diagnosis in multiple working conditions by using dynamic routing technology as the federation strategy for federated learning of the multiple modular neural network. First, according to different working conditions, the collected multi-working condition data are divided into different groups for feeding of modular network to extract the local features under different working conditions. Then, an additional deep neural network is constructed to extract the feature involved in data without working condition division. Finally, the global adaptive feature extraction of each working condition can be obtained by designing a federated strategy based on dynamic routing technology to achieve the weights allocation scheme of the modular neural network. The bearing dataset of Case Western Reserve University is taken as a benchmark dataset to verify the effectiveness of the proposed method.