Knowledge-informed multiplication convolution generalization network for interpretable equipment diagnosis under unknown speed domains

The generalization performance and interpretability of intelligent fault diagnosis methods under unknown speed domains are crucial concerns in real industry practice. However, existing solutions seldom address both issues simultaneously, restricting their development prospects. Motivated by these challenges, this study puts forward a signal-processing-collaborated deep learning architecture—knowledge-informed multiplication convolution generalization network (KI-MCGN), which is composed of three layers, called adaptive mode capturer (AMCer), prior knowledge pooler (PKPer) and classifier. Informed by the mode response characteristics of fault vibration signals, AMCer first tailors several speed-fused multiplication filtering kernels (SF-MFKs) for adaptive mining of fault-related modes. To improve the generalization capability, the center frequency and bandwidth coefficient of SF-MFKs are no longer defined directly, but are innovatively fitted by multiple trainable coefficients with regard to the speed information. This novel speed fusion strategy allows SF-MFK to not only learn the mapping relationship between the speed information and the distribution of fault-included modes, but also to autonomously adjust its modal filtering scale in unknown speed domains. In light of the excellent comprehensibility of prior indicators in characterizing the health status of equipment, a novel pooler named PKPer is presented subsequently. It pools each extracted mode into 12 frequency-domain modal prior indicators (MPIs). Eventually, two dense layers are adopted as the classifier to output the ultimate decision. In particular, considering the distribution difference of mode features across different speed domains, local-domain generalization is further integrated to assist the model extract generalized features. The comparison results from two experimental cases demonstrate that the proposed KI-MCGN architecture outperforms the other eight state-of-the-art approaches and three ablation models. Meanwhile, comprehensive visualization analysis not only validates the modal filtering potency of SF-MFKs under unknown speed domains, but also explores the guiding meaning of MPIs for the final diagnosis. It can be also foreseen that the proposed KI-MCGN framework is expected to provide reliable and explainable intelligent decision-making for equipment maintenance under unknown speed domains.