Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions

The speed variation poses great hardships to the intelligent fault diagnosis of mechanical equipment. Existing solutions rarely consider the interpretable representation of fault information, and still suffer from the "black box" issue that weakens their own practicability and credibility. Motivated by these issues, this study mathematically derives the modal response model of fault-impulsive signals of rotating machinery under variable speed conditions. On this foundation, an interpretable architecture collaborating signal processing with deep learning—prior-knowledge-guided mode filtering network (PKG-MFNet) is proposed, which consists of three sub-structures: mode filtering layer, prior knowledge pooling layer and classifier. In the mode filtering layer, multiple FIR filtering kernels are first constructed by an explainable speed fusion strategy. Each filtering kernel has a center frequency and a bandwidth coefficient fitted from the speed, which is used to extract fault-sensitive modes under varying speed conditions as the explainable feature representations of fault information. Subsequently, the extracted modes are pooled into 12 modal prior indicators (MPIs) that represent health status information in the prior knowledge pooling layer. Finally, the classifier employs two fully-connected layers to make the final decision. Different from the conventional interpretable network for speed information characterization, the speed information is innovatively fused into a novel mechanism framework for sensitive information mining with rigorous theoretical support. Extensive experiments are conducted to verify the superiority of the proposed method over six state-of-the-art methods. Particularly, the visualization analysis not only demonstrates the mode filtering capacity of FIR filtering kernels under variable speed conditions, but also interprets the guiding significance of MPIs for the final decision.