Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis

Due to the neglect of prior characteristics and the lack of explicit constraints on fault knowledge, conventional intelligent diagnosis methods suffer from great hardships in exacting fault-sensitive information and making explainable decisions, resulting in poor interpretability and inferior robustness. Motivated by the excellent multiscale analysis performance of signal processing and the powerful feature mining ability of deep learning, this study proposes an interpretable FIRNet for equipment intelligent diagnosis under strong noise environments. It consists of a well-crafted FIRLayer and a deep learning backbone. Inspired by the modulation principle that fault-sensitive components are normally modulated into multiscale mode characteristics, multiple ex-ante interpretable filters with two learnable parameters, including center frequency and bandwidth, are analytically designed to process the sequence signal sets, represented as signal-processing-based FIRLayer where the extracted multiscale feature maps are taken as an interpretable status information expression. Subsequently, multiscale convolutional kernels are established to extract the high-level feature maps and further make the final diagnostic decisions, represented as the deep learning backbone. The simulated and experimental results show that the proposed FIRNets have higher identification precision compared to the other nine deep learning models. Specially, three aspects, including model interpretability, noise robustness capacity and edge intelligent diagnosis, are further analyzed to illustrate the interpretable advantages of FIRNets. Hereinto, feature visualizations of the FIRLayer and mode decision contributions of the deep learning backbone are in-depth analyzed to verify the interpretable feature representation and decision-making principle of FIRNets. The results indicate that FIRNets have superior ex-post interpretability compared to other methods. Focusing on industrial practices, an efficient edge diagnosis system based on a pruned FIRNet is established, and an online diagnosis accuracy of more than 99% has been achieved. It can be foreseen that the proposed FIRNets show great potential and competitiveness to promote the edge computing application of equipment intelligent diagnosis.