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Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2157–2170 | Cite as

RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults

  • D. BenmahdiEmail author
  • L. Rasolofondraibe
  • X. Chiementin
  • S. Murer
  • A. Felkaoui
Article

Abstract

The complexity of the current installations requires advanced and effective monitoring techniques. The most commonly used technique is the vibratory analysis. Despite the large number of existing methods for detection, diagnosis and monitoring of bearing defects, the scientific community is widely interested in learning methods. These methods allow automatic detection and reliable diagnosis. This paper presents anew real-time unsupervised pattern recognition approach for the detection and diagnosis of bearings defects: RT-OPTICS. This approach focuses on two steps of damage evolution: defect detection by classification and monitoring of the new cluster representing the degraded state of the bearing. These two steps are performed by a two-dimensional method implementing scalar indicators: Kurtosis and Root Mean Square values. These two indicators provide additional information about the presence of defects in the bearing. The first step deploys RT-OPTICS based on the real-time unsupervised ordering points to identify clustering structure (OPTICS) classification to detect defects on inner and/or outer bearing races. The next step is to monitor the state of degradation using three parameters of the new cluster: the center jump, density and contour of this cluster. After a validation on simulated signals which variations of parameters were tested, this approach was tested under experimental conditions on a test bench made up of N.206.E.G15bearings, with varying load and angular velocity. A comparative study is carried out between the suggested approach and (i) a classical approach: monitoring of scalar indicators over time and (ii) a dynamic classification method (DBSCAN).

Keywords

Vibratory analysis Diagnosis and monitoring Bearing Unsupervised classification OPTICS 

Notes

Acknowledgements

The authors would like to thank the Algerian Ministry of Higher Education and Scientific Research for financial support in the framework of the PNE 2015-2016 Program.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • D. Benmahdi
    • 1
    Email author
  • L. Rasolofondraibe
    • 2
  • X. Chiementin
    • 3
  • S. Murer
    • 3
  • A. Felkaoui
    • 1
  1. 1.Applied Precision Mechanics Laboratory, Institute of Optics and Precision MechanicsSetif -1- UniversitySétifAlgeria
  2. 2.CReSTICUniversity of Reims Champagne ArdenneReims Cedex 2France
  3. 3.GRESPIUniversity of Reims Champagne ArdenneReims Cedex 2France

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