Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network

Bearing health state recognition is often affected by variable speeds and heavy load working conditions, making fault signal features difficult to identify and resulting in challenges in health condition recognition methods, including feature extraction difficulties and cross-device recognition challenges. This paper proposes the Bearing Health Status Evaluation Method Based on Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network. A multi-scale feature extraction method is developed, and a Multi-Scale Hybrid Features and Inception-Block Attention Bidirectional Physics-Informed Domain Adaptation Network is introduced. This network uses physical information layers and inverse physical information layers to constrain multi-scale features and adaptively adjust model hyperparameters, incorporating Inception multi-scale convolution and convolutional self-attention mechanisms to enhance feature recognition capabilities. To validate the effectiveness of the model, this paper constructs a Health Status Dynamic Time Warping-Mic Index and uses the Xi’an Jiao tong University bearing degradation dataset, PHM2012 challenge dataset, and centrifugal pump engineering data for model validation. The results demonstrate that the model performs well in recognizing the health status of equipment under cross-operating conditions and cross-device scenarios.