A cointegration-based monitoring method for rolling bearings working in time-varying operational conditions

Ali Akbar Tabrizi, Hussein Al-Bugharbee, Irina Trendafilova, Luigi Garibaldi

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Most conventional diagnostic methods for fault diagnosis in rolling bearings are able to work only for the case of stationary operating conditions (constant speed and load), whereas, bearings often work at time-varying conditions. Some methods have been proposed for damage detection in bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods can only be applied for limited speed variations. In this study, a novel combined method for fault detection in rolling bearings based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. The method makes use solely of the measured vibration signals and does not require any additional measurements while it can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into zero-mean modes called intrinsic mode functions using the performance improved ensemble empirical mode decomposition method. Then, the cointegration, which is finding stationary linear combination of some non-stationary time series, is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager–Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using the one-class support vector machine method. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results confirm that the method can be successfully applied to distinguish between healthy and faulty bearings even if the shaft speed changes dramatically.

LanguageEnglish
Pages1-17
Number of pages17
JournalMeccanica
Early online date17 May 2016
DOIs
Publication statusE-pub ahead of print - 17 May 2016

Fingerprint

Bearings (structural)
Monitoring
hyperplanes
vibration
Defects
fault detection
Damage detection
defects
Fault detection
Failure analysis
Support vector machines
Time series
damage
Decomposition
costs
decomposition
operators

Keywords

  • cointegration
  • one-class support vector machine
  • performance improved ensemble empirical mode decomposition (PIEEMD)
  • rolling bearings fault detection
  • Teager–Kaiser energy operator (TKEO)
  • time-varying operating conditions

Cite this

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abstract = "Most conventional diagnostic methods for fault diagnosis in rolling bearings are able to work only for the case of stationary operating conditions (constant speed and load), whereas, bearings often work at time-varying conditions. Some methods have been proposed for damage detection in bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods can only be applied for limited speed variations. In this study, a novel combined method for fault detection in rolling bearings based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. The method makes use solely of the measured vibration signals and does not require any additional measurements while it can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into zero-mean modes called intrinsic mode functions using the performance improved ensemble empirical mode decomposition method. Then, the cointegration, which is finding stationary linear combination of some non-stationary time series, is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager–Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using the one-class support vector machine method. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results confirm that the method can be successfully applied to distinguish between healthy and faulty bearings even if the shaft speed changes dramatically.",
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A cointegration-based monitoring method for rolling bearings working in time-varying operational conditions. / Tabrizi, Ali Akbar; Al-Bugharbee, Hussein; Trendafilova, Irina; Garibaldi, Luigi.

In: Meccanica, 17.05.2016, p. 1-17.

Research output: Contribution to journalArticle

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