Paper Info
Title
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer.
Abstract
An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.
Year
DOI
Venue
2018
10.3390/s18041128
SENSORS
Keywords
Field
DocType
Model-reference fault diagnosis,bearing fault diagnosis,super-twisting higher-order sliding mode observation technique,ARX-Laguerre proportional integral observation method
Data modeling,Nonlinear system,Control theory,Electronic engineering,Bearing (mechanical),Bearing fault detection,Acceleration,Vibration,Engineering,Observer (quantum physics)
Journal
Volume
Issue
ISSN
18
4.0
1424-8220
Citations 
PageRank 
References 
3
0.41
22
Authors
2
Name
Order
Citations
PageRank
Farzin Piltan144.81
Jong Myon Kim214432.36