Multi-Sensor Dictionary Fusion-Driven Sparse Representation Classification Method for Gearbox Fault Diagnosis

Abstract

As a powerful tool for sparse signal representation, dictionary learning has gained traction in data-driven fault diagnosis. However, noise interference exists in actual industrial environments; noise as a common feature across all classes inevitably covers discriminative features, thereby impairing the diagnostic performance of methods reliant on dictionary learning. To tackle this challenge, this paper proposes a multi-sensor dictionary fusion-driven sparse representation classification method. The initial step involves leveraging a low-rank shared dictionary learning technique to extract discriminative dictionaries from multi-sensor data. A dictionary fusion metric is introduced based on dictionary reconstructability, dictionary discriminativity, and sparse code discriminability to fuse dictionaries from different sensors. Second, a sparse code discriminative enhancement method is proposed, which suppresses sparse code in non-correct projection regions by setting them to zero, thereby reducing the contribution of sparse code from incorrect projection areas. A classification framework grounded in sparse representation is then formulated. Validation experiments on two distinct planetary gearbox datasets (a laboratory planetary gearbox dataset and the southeast university planetary gearbox dataset) reveal that the multi-sensor dictionary fusion-driven sparse representation classification method attains 99.95% and 99.94% classification accuracy. Comparative analyses further substantiate its exceptional diagnostic precision against contemporary advanced techniques.