面向高效通信联邦学习的设备故障诊断方法

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Abstract：Federated learning achieves joint trairung modeling of fauh data from various factories while protecting privacy.However， due to the high heterogeneity of factory equipment operation data， traditional federated learning has low communicationefficiency. To address the above /ssues， an equ/pment fault diagnosis method based on high-efficiency communication federatedlearning was proposed. Firstly， a federated dynamic weighted balancing model was proposecl， which dynamically adjusts the trainingfrequency and uploaded parameter amount of the factory sub end， and improves communication efficiency by reducing communicationtime-. Secondly， a dual jump gate， cyclic unit diagnostic ruode，l with an attention mechanism was proposecl， which assigns differentweights to diffe，rent features to quickly extract fault features， effectively shortening communication rounds and improving communicationeffic，iency. Finally， experimental validation was conducted using the bearing failure datasets of Case Western Reserve University andPaderborn University. The experimental results show that compared with the federated average algorithm， this method achieves a faultdiagnosis accuracy of 92. 20% ， while reducing communication time by 56. 88%  and shortening communic，ation rounds by 47. 37% ，effectively improving communication efficiency.

Keywords：fault diagnosis; federated learning; data heterogeneity; corrununication efficiency; dynamic weighting

0  引  言

联邦学习作为分布式机器学习新范式，在保护数据隐私的基础上实现联合多参与方共同建模，进而有效解决数据孤岛问题。（剩余14005字）