An adaptive thresholding approach for open set fault diagnosis

作者：Cheng Wang, Bingyou Cheng, Lili Deng · 发表于：Measurement Science and Technology · 年份：2024 · DOI：10.1088/1361-6501/ad9625 · 被引用次数：6 · 研究领域：Machine Fault Diagnosis Techniques、Fault Detection and Control Systems、Mechanical Failure Analysis and Simulation

Abstract Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k -nearest neighbors based adaptive thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the pote...