Abstract
 

Fault diagnosis of rolling element bearings is essential to ensure the safety and reliability of industrial sites. However, changes in operating conditions can lead to variations in the distributions of the data that is collected for fault diagnosis. This, in turn, decreases the performance of deep-learning-based fault-diagnosis methods. In addition, most data in industrial settings are unlabeled, which leads to ineffectiveness of the supervised learning method. To address the issues of domain shift and unlabeled data, numerous studies have been conducted to reduce distribution discrepancies when using unlabeled data. Still, most of these studies assume that the number of labels in the training and test data are identical; this is not always true for data from industrial sites. Thus, the research outlined in this paper was pursued to address the partial domain adaptation problem, which occurs when there are fewer labels in the test data than in the training data. The proposed approach suggests two methods for applying partial domain adaptation in mechanical systems: i) a domain knowledge filter is proposed, which reflects fault characteristics in the original signal for effective feature extraction in the mechanical engineering domain, and ii) a gradient alignment module is defined to align the gradient of the statistical loss function. The method proposed herein was validated using two open-source datasets; the approach demonstrated high performance and low uncertainty, as compared to other prior methods. Additionally, physical analysis of the domain knowledge filter was conducted in this work.