Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate concepts for individual use cases and specific data types. In this work, we investigate how to leverage established concept-based explanation techniques in the context of bearing fault detection with deep neural networks trained on vibration signals. Since bearings are prevalent in almost every rotating equipment, ensuring the reliability of intransparent fault detection models is crucial to prevent costly repairs and downtimes of industrial machinery. Our evaluations demonstrate that explaining opaque models in terms of vibration concepts enables human-comprehensible and intuitive insights about their inner workings, but the underlying assumptions need to be carefully validated first.

翻译：概念基解释方法（如概念激活向量）是量化输入数据中抽象或高层特征如何影响复杂深度神经网络预测的有效工具。然而，将其应用于工业预测问题具有挑战性，因为对于特定用例和具体数据类型，如何定义并获取适当的概念并不明确。本研究探讨如何在基于振动信号训练的深度神经网络轴承故障检测中，利用已建立的概念基解释技术。由于轴承几乎存在于所有旋转设备中，确保不透明故障检测模型的可靠性对于预防工业机械代价高昂的维修和停机至关重要。我们的评估表明，用振动概念解释不透明模型能够提供对人类可理解且直观的内部运行机制洞察，但首先需要仔细验证其潜在假设。