Gearbox fault diagnosis based on temporal shrinkage interpretable deep reinforcement learning under strong noise

Abstract

Gearbox fault diagnosis is crucial for the safe operation of mechanical systems. While Deep Learning (DL) has demonstrated promising results in this area, most existing methods rely on static supervised learning, lacking the dynamic, interactive learning capabilities similar to human decision-making. To tackle this issue, this study presents a novel approach that combines the strengths of Deep Reinforcement Learning (DRL) with the interpretability of a temporal shrinkage interpretable network. DRL integrates the perception abilities of DL with the decision-making capabilities of Reinforcement Learning (RL), offering a more comprehensive solution for complex challenges. In this method, gearbox fault diagnosis is formulated as a sequential decision problem within a Classification Markov Decision Process (CMDP). A multi-scale temporal shrinkage module is utilized to construct an interpretable network, which enhances model interpretability and reduces the negative impact of noisy data in harsh working conditions. The diagnosis agent autonomously learns the optimal classification policy, reducing the need for manual intervention and human expertise. Experimental results show excellent generalization and stability, achieving over 98.5% accuracy even in noisy conditions. It outperforms existing methods and highlights its robustness in challenging operational environments.