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Journal of Failure Analysis and Prevention

, Volume 17, Issue 5, pp 1053–1066 | Cite as

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

  • Mohammed Chalouli
  • Nasr-eddine Berrached
  • Mouloud Denai
Technical Article---Peer-Reviewed

Abstract

Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.

Keywords

Failure diagnosis Bearing faults Time-domain features Condition-based maintenance Health indicators Relevant features Fault feature extraction 

Notes

Acknowledgments

The authors wish to thank NSF I/UCR Center for Intelligent Maintenance Systems (IMS) [21] and Case Western Reserve University [40] for providing free access to the bearing vibration experimental data from their Web sites.

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Copyright information

© ASM International 2017

Authors and Affiliations

  1. 1.Intelligent Systems Research Laboratory (LARESI)USTO-MB UniversityOranAlgeria
  2. 2.School of Engineering and TechnologyUniversity of HertfordshireHatfieldUK

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