Incremental Single-Class Fault Detection and Diagnosis Method for Rolling Bearings Based on OS-ELM

Abstract

Aiming at the problem of current equipment fault diagnosis models based on deep learning being unable to automatically identify new class faults according to the updated fault data, in this paper we propose an incremental single-class fault diagnosis method based on an online sequential extreme learning machine (OS-ELM). In addition to detecting new types of faults, this method can perform class-incremental learning based on new-class fault data, treating the new-class faults as known faults for ongoing fault detection and diagnosis tasks. This approach first constructs a feature extraction network with a dual-encoder structure to extract data features. Subsequently, the extracted features are used to build a fault diagnosis network based on OS-ELM, where the novelty of new batches of data is determined by the update magnitude of OS-ELM. When a new-class fault is detected, a new OS-ELM representing the current new class is constructed using the new batch of data and added to the fault diagnosis network, thereby achieving incremental model updates. The proposed method is validated through experiments on the CWRU dataset and MFPT dataset. The results demonstrate that the accuracy of this method on the CWRU dataset is 99.62%, while on the MFPT dataset it reaches 98.80%. Compared to other incremental single-class models, this method exhibits excellent fault recognition and diagnosis capabilities.