Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/238561

Title

Anomaly detection of rolling elements using fuzzy entropy and similarity measures

Author(s)

Wong, M. L. D.;  Lee, S. H.;  Nandi, A. K.

Abstract

The ability of detecting faults in rotating elements is highly desired in machine condition monitoring application (MCM). On many MCM platforms, discriminating attributes based on time and/or frequency domain of the acquired vibration data are used to classify the element under monitoring into normal and abnormal conditions. However, having such diagnostic ability is still insufficient in our global goal towards predictive maintenance. To achieve true predictive maintenance, the development tool must be able to provide a certain level of real time computation capability. In this paper, the authors propose a novel method based on fuzzy entropy and similarity measure for monitoring the health conditions of ball bearings on-line. The practicalities of the effectiveness and speed of the method are verified empirically, and results are presented towards the end of this paper.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Sarawak Campus. School of Engineering, Computing and Science

Source

Proceedings of the 10th International Conference on Vibrations in Rotating Machinery, London, United Kingdom, 11-13 September 2012, pp. 693-702

Publication year

2012

Keyword(s)

Anomaly detection;  Ball bearings;  Fuzzy entropy;  Machine condition monitoring;  Rolling elements;  Rotating elements;  Similarity measures

Publisher

Woodhead Publishing

ISBN

9780857094520, 0857094521

Publisher URL

http://www.woodheadpublishing.com/en/book.aspx?bookID=2781

Copyright

Copyright © The author(s) and/or their employer(s) unless otherwise stated, 2012.

Peer reviewed