Contextual Knowledge-Informed Deep Domain Generalization for Bearing Fault Diagnosis

Reliable methods for bearing fault diagnosis are of great importance because they provide the possibility of preventing failures in machines. A significant challenge is developing solutions that can handle the variances in the data across different domains i.e., the operational context of the bearing and settings including noise, bearing type, rotational speed, and sampling frequency. To solve this issue, a common approach is to use transfer learning. However, most existing methods either assume that faults are available in the target domain or that only operational conditions on a single machine are changed between the source and target domain. Unfortunately, neither of these assumptions can be made in an industrial environment where there is a need to deploy the method on new machines with no historical faults. Therefore, there is a need to develop methods for bearing fault diagnosis that can provide accurate predictions on a general basis without needing access to historical faults from the target domain. To address this problem, this study develops a novel method including a knowledge-enriched standardization procedure used to lower the domain shift, feature-targeted metric learning enriched with contextual data that can be deployed to any new domain without having access to historical faults. The method is compared against state-of-the-art solutions in three cases with various tasks where it achieves the highest accuracy and displays high robustness and generalizability. The results also show a low level of false positives, a high level of fault segment recall and the possibility of giving feedback to the user about the model’s decision. The conclusion is that the method can be used on a general basis for fault diagnosis of bearings without having access to historical faults from the target domain.