Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning
This work addresses the need for explainable AI in industrial applications like bearing fault detection, offering a template for similar problems, though it is incremental in enhancing existing XAI techniques.
The authors tackled the problem of making deep learning models for bearing fault detection more interpretable to domain experts by proposing a domain-specific feature attribution framework that validates model logic against expert reasoning and anticipates generalization ability.
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods poses a major obstacle for real-world deployment. Explainable AI (XAI) and especially feature attribution techniques promise to enable insights about how such models form their decision. But the plain application of such methods often fails to provide truly informative and problem-specific insights to domain experts. In this work, we focus on the specific task of detecting faults in rolling element bearings from vibration signals. We propose a novel and domain-specific feature attribution framework that allows us to evaluate how well the underlying logic of a model corresponds with expert reasoning. Utilizing the framework we are able to validate the trustworthiness and to successfully anticipate the generalization ability of different well-performing deep learning models. Our methodology demonstrates how signal processing tools can effectively be used to enhance Explainable AI techniques and acts as a template for similar problems.