Adaptive parametric dictionary design of sparse representation based on fault impulse matching for rotating machinery weak fault detection

Abstract

Fault detection and diagnosis techniques for rotating mechanical components are crucial for the safety, efficiency, reliability of mechanical systems. In recent decades, many sparse representation approaches based on parametric dictionary design have been successfully applied to extract fault features from vibration signals. However, most of them rely on the classical correlation filtering algorithm (CFA), which has some shortcomings, such as poor antinoise ability and an extensive computation load. To address these issues, this paper proposes a novel water cycle algorithm (WCA)-optimized fault impulse matching algorithm (FIMA) for parametric dictionary design, which can match the underlying fault impulse structure of fault signals by applying a comprehensive strategy of local matching and global matching. With the proposed method, the accuracy of wavelet parameter identification and calculation efficiency are improved significantly. In addition, the method is suitable for constructing parametric dictionaries with different wavelet bases according to different rotating machinery components. The effectiveness of the proposed method is verified by the simulated signals, as well as the practical wheelset bearing faults (outer race fault and inner race fault) and gearbox (broken tooth) signals. The results of a comparison study show that the proposed method outperforms the classical CFA, K-SVD and whale optimization algorithm-optimized orthogonal matching pursuit in weak fault feature extraction.