A Multi-scale Attention Mechanism Diagnosis Method with Adaptive Online Updating Based on Deep Learning under Variable Working Conditions

With the advance of industrial systems, the online equipment fault diagnosis has encountered many challenges such as data drift and data imbalance under varying operating conditions, thus making stable and accurate diagnosis increasingly critical. Considering the above issues, a multi-scale attention mechanism diagnosis method with adaptive model that can be updated based on deep learning has been proposed. The method is composed of four main steps: training the multi-scale offline diagnosis model, transferring the parameters of the offline model, assessing the degree of data drifting, and adaptively updating the diagnostic model. A data balance strategy with adaptive weight balances both inter-class and intra-class data. The method updates the diagnostic model flexibly according to online data status, to reduce the impact of data drifting. The method was verified on a bearing test rig, which can reproduce the common bearing faults under variable working conditions. The experimental results have shown that the proposed method can accurately and reliably identify the bearing faults.