Digital twin-driven attention-guided convolutional networks for intelligent fault diagnosis across different domains

Abstract

Fault diagnosis of rolling bearings is crucial in industries to ensure the reliability, safety, and economic feasibility of mechanical systems. Conventional data-driven methods for fault diagnosis depend on pre-existing datasets containing various failure modes for training. However, obtaining such datasets can be challenging, especially in critical industrial environments, hindering the applicability of these methods across different scenarios. To address this challenge, this research introduces a novel approach called a digital twin-driven attention-guided convolutional network (DTACN) for bearing fault diagnostics with limited data. This study offers two significant enhancements: (1) Development of an intricate digital twin model for bearings, which includes multi-scale Kinetic simulations of the bearing’s operational parameters. This model utilizes the architectural characteristics of the bearing and the severity of the failure to predict the vibratory system’s response accurately; and (2) Establishment of a domain adaptation-based framework employing deep convolutional models and attention mechanism to transfer knowledge from simulated signals to real signals. This framework facilitates efficient fault recognition even in scenarios with limited prior knowledge. The effectiveness of the proposed approach has been rigorously validated through comprehensive experiments.