Open-Set Fault Recognition and Inference for Rolling Bearing Based on Open Fault Semantic Subspace

Intelligent fault diagnosis methods have gained much attention in industry. An important premise of these methods is that the training and test data maintain the same set of fault classes, known as the closed-set hypothesis, which, however, cannot be guaranteed in fault diagnosis tasks. This can result in potentially unknown faults being incorrectly randomly classified as a known fault (KF) class. To overcome this problem, we introduce open-set recognition and proposed an open fault semantic subspace-based open-set fault diagnosis and inference framework (OFS-FDI), which identifies unknown faults while completing the diagnosis of a known class of faults, and furthermore, inferring the possible fault type of unknown samples. First, a fault semantic subspace construction method is proposed to transform the original signal into a set of low-dimensional representation subsets conforming to a conditional Gaussian distribution, which is related to the fault semantic. Then, an outlier score is proposed to determine whether a sample is from an unknown class. Finally, a class directional index (CDI) metric is proposed to perform inferential analysis of possible fault classes. In the experiments based on two typical rotating machines, the outlier detection accuracy of OFS-FDI is improved by up to 0.04% compared with the comparative methods. The accuracy for unknown fault inference (UFI) is up to 99.65%.