Matching Pursuit Network: An Interpretable Sparse Time-Frequency Representation Method Toward Mechanical Fault Diagnosis

Abstract

Rotatory machinery commonly operates in complex environments with strong noise and variable working conditions. Time-frequency representation offers a valuable method for capturing and analyzing nonstationary characteristics, making it particularly suitable for identifying transient fault-related features. However, despite these advantages, extracting robust and interpretable fault features in machinery operating under variable speeds remains a challenge with existing techniques. In this article, a novel sparse time-frequency representation (STFR) method, named matching pursuit network (MPNet) is proposed for mechanical fault diagnosis. First, a deep network structure with signal decomposition capability is constructed by well-defined interpretable matching pursuit (MP) units to automatically learn discriminative features from time-frequency inputs. Then, the weights of each effective component signal to reconstruct the raw input are designed to measure their contributions. Accordingly, the optimization criterion with structural similarity metric is produced to realize the model parameter update in an end-to-end manner. Finally, phenomenological model-based fault simulation signals and real fault signals from gearbox experiments are used for model training and testing, respectively. The results show that the proposed approach can well extract robust and interpretable time-frequency features and obviously outperforms the state-of-the-art time-frequency representation methods.