Domain-Knowledge-Driven Intelligent Attribute Definition for Zero-Shot Fault Diagnosis of Bearings

To address the issue in zero-shot fault diagnosis (ZSFD) where fault attribute definitions (FADs) rely heavily on manual design and the accuracy of FAD depends on the expertise of developers, this article embedded expert knowledge into deep learning network, proposed a ZSFD method based on depth correlation feature extraction network (DCFEN), and automatically constructed FAD. Taking advantage of the periodic characteristics of bearing fault signals and the advantages of correlation analysis operation (CAO) in periodic signal analysis, DCFEN extracts the periodic characteristics of input signals in multiple dimensions by integrating CAO with deep learning. In addition, a soft-threshold-based feature percolation mechanism and FAD evaluation function are designed to generate the attributes related to bearing faults. The experimental results show that the FADs established by DCFEN are accurate, and the fault diagnosis performance of the proposed ZSFD is superior to the existing methods in unseen scenarios.