ORgram: semi-supervised learning framework for inline bearing diagnosis in varying speed
Journal
The International Journal of Advanced Manufacturing Technology
Journal Volume
134
Journal Issue
5-6
Start Page
2387
End Page
2401
ISSN
0268-3768
1433-3015
Date Issued
2024-08-16
Author(s)
DOI
10.1007/s00170-024-14235-x
Abstract
Based on the fast kurtogram, many previous studies have focused on frequency band selection (FBS) affected by interference due to impulsive noise and varying speed conditions. Recently, machine learning algorithms have been considered effective for bearing diagnosis. However, most of these methods are supervised learning and thus suffer from data imbalance in the practical applications. This study proposes a semi-supervised learning indicator called outlier rate diagram (ORgram) based on the signal changes before and after bearing damage. ORgram is suitable for impulsive noise and varying speed conditions. First, the healthy bearing data is segmented by rotational speed and modeled with one-class support vector machine (SVM) for each frequency band. As bearing defects sprout, the defect response will produce the highest outlier rate in the informative frequency band (IFB). After selecting the IFB, techniques such as envelope analysis and order tracking are used to identify damaged bearing components for an alarm to reduce the losses caused by unplanned downtime.
Subjects
Inline bearing diagnosis
Non-Gaussian noise
Novelty detection
Support vector machine
Varying speed
Publisher
Springer Science and Business Media LLC
Type
journal article