Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

Abstract

Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

“…Concerning this issue, Li [ 25 ] proposed a semi-supervised weighted kernel clustering algorithm based on gravitational search for bearing fault diagnosis and processed the unlabeled samples by calculating the weighted kernel distances among them and fault cluster centers. Qin et al [ 26 ] employed a tritraining method for bearing fault diagnosis. Zhao et al [ 27 ] proposed a new graph-based semi-supervised classification for bearing fault diagnosis with sparse coding method.…”