Fault Feature Extractor Based on Bootstrap Your Own Latent and Data Augmentation Algorithm for Unlabeled Vibration Signals
Tengyi Peng, Changqing Shen, Shilong Sun, Dong Wang
Abstract
Given that vibration fault signals collected from industrial circumstances are usually insufficient and have no labels, supervised learning networks cannot be directly applied to recognize fault types in this case. Hence, automatic feature extraction of unlabeled data is urgently needed. In this study, an automatic fault feature extractor (AFFE) based on the contrastive learning algorithm—Bootstrap Your Own Latent (BYOL) network, which can extract fault features automatically without needing labeled information—is proposed. A data augmentation method for vibration signals is studied because it is critical to the contrastive learning algorithm. This study determines a data augmentation combination that can help AFFE achieve excellent performance in extracting features from unlabeled bearing fault data. To verify the validity of the proposed method, we utilize some labeled data (5% of samples with labels in a dataset) to fit linear classifiers, which are combined with the proposed AFFE to extract a feature. The aim is to predict the accuracy score of feature classification for the remaining data. The case study demonstrates that the fault features extracted using AFFE can achieve a high accuracy score of 94.81%. Therefore, the proposed AFFE-BYOL is a promising diagnostic fault feature extraction scheme to process unlabeled vibrational data.