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.

翻译：深度学习已在多种相关工业场景中成功应用于分析传感器数据。然而，许多高性能方法的黑箱特性成为其实际部署的主要障碍。可解释性人工智能（XAI）特别是特征归因技术有望揭示模型决策机制，但直接应用这些方法往往无法为领域专家提供真正有价值的、针对具体问题的见解。本研究聚焦于从振动信号检测滚动轴承故障这一特定任务，提出了一种新颖的领域特定特征归因框架，用于评估模型底层逻辑与专家推理的契合程度。利用该框架，我们能够验证不同高性能深度学习模型的可信度，并成功预测其泛化能力。本研究方法展示了信号处理工具如何有效增强可解释性人工智能技术，并为类似问题提供了可复用的模板。