Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer
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 bea...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/18/4/1128 |
id |
doaj-bc56763196604f30be401490a22a9c29 |
---|---|
record_format |
Article |
spelling |
doaj-bc56763196604f30be401490a22a9c292020-11-25T02:30:51ZengMDPI AGSensors1424-82202018-04-01184112810.3390/s18041128s18041128Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode ObserverFarzin Piltan0Jong-Myon Kim1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 680-479, KoreaSchool of IT Convergence, University of Ulsan, Ulsan 680-479, KoreaAn 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.http://www.mdpi.com/1424-8220/18/4/1128Model-reference fault diagnosisbearing fault diagnosissuper-twisting higher-order sliding mode observation techniqueARX-Laguerre proportional integral observation method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Farzin Piltan Jong-Myon Kim |
spellingShingle |
Farzin Piltan Jong-Myon Kim Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer Sensors Model-reference fault diagnosis bearing fault diagnosis super-twisting higher-order sliding mode observation technique ARX-Laguerre proportional integral observation method |
author_facet |
Farzin Piltan Jong-Myon Kim |
author_sort |
Farzin Piltan |
title |
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer |
title_short |
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer |
title_full |
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer |
title_fullStr |
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer |
title_full_unstemmed |
Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer |
title_sort |
bearing fault diagnosis by a robust higher-order super-twisting sliding mode observer |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-04-01 |
description |
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. |
topic |
Model-reference fault diagnosis bearing fault diagnosis super-twisting higher-order sliding mode observation technique ARX-Laguerre proportional integral observation method |
url |
http://www.mdpi.com/1424-8220/18/4/1128 |
work_keys_str_mv |
AT farzinpiltan bearingfaultdiagnosisbyarobusthigherordersupertwistingslidingmodeobserver AT jongmyonkim bearingfaultdiagnosisbyarobusthigherordersupertwistingslidingmodeobserver |
_version_ |
1724827372917620736 |