Generalized Cross-Severity Fault Diagnosis of Bearings via a Hierarchical Cross-Category Inference Framework

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a challenging task for which the target fault severities do not exist in the training samples, this article proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods.