Physics-mechanism consistent fault diagnosis of rolling bearings: a multi-channel image fusion and quantitative interpretable framework

Abstract

In complex industrial environments, data-driven fault diagnosis of rolling bearings is often constrained by time-varying operating conditions, severe noise interference, and the ‘black-box’ nature of deep learning models. To overcome these challenges, this study systematically integrates an improved Harris Hawks optimization (IHHO) algorithm, a convolutional neural network, and a bidirectional long short-term memory network to establish a physics-consistent fault diagnosis framework. First, a multi-channel image fusion architecture based on continuous wavelet transform is constructed to effectively encode the deep spatiotemporal correlations embedded in triaxial vibration signals. To enhance model adaptability, a Cauchy mutation–based IHHO strategy is introduced for autonomous hyperparameter optimization, effectively alleviating premature convergence to local optima. The core innovation of this work lies in the development of a quantitative interpretability framework centered on a novel metric termed the energy capture ratio (ECR). By jointly employing SHAP and gradient-weighted class activation mapping visualization techniques, the proposed model is demonstrated to exhibit strong physical consistency, enabling adaptive suppression of noise-dominated channels while accurately focusing on theoretically relevant fault resonance frequency bands. Extensive experiments on the HUST, case western reserve university, and southeast university benchmark datasets demonstrate that the proposed framework achieves average diagnostic accuracies of 99.0%, 100.0%, and 99.0%, respectively; even under severe noise interference at −5 dB, the accuracy remains as high as 89.6%. Quantitative evaluations show that the principal-axis relative ECR (RECR) consistently remains above 1.0 across all fault conditions, providing strong evidence for the mechanistic reliability of the model. These findings indicate that the proposed method provides an accurate and reliable solution for predictive maintenance of bearings in practical engineering applications.