Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying working conditions

Mechanical equipment such as wind turbines often operates under time-varying working conditions (TVWC). The vibration signals collected from their key rotating components, such as bearings and gears, are often affected by TVWC. In this situation, eliminating the influence of working conditions is the key to effectively implementing end-to-end intelligent diagnosis of rotating machinery (RM). This paper first introduces the transformer encoder architecture to construct a diagnostic model. Then an improved self-attentive module based on a depth-separable convolution operation is built to encode better condition-independent depth diagnostic features and reduce the number of model parameters. Thus, a convolution enabled Transformer (Con-eT) is constructed as a deep encoder for the diagnostic model. Subsequently, a random contrastive regularization (RCR) method inspired by recently proposed contrastive learning is proposed to incentivize the model to learn working-condition-independent features and to improve the model's working condition generalization performance. A publicly available dataset of bearings at time-varying speeds is adopted to verify the effectiveness of the proposed method. Meanwhile, a specific experiment with a wind turbine gearbox designed explicitly for time-varying speed conditions further demonstrates the advantage of the proposed method over the comparative method in terms of the model's working condition generalization.