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Home List of Titles Anomaly detection of rolling elements using fuzzy entropy and similarity measures
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/238561
- Anomaly detection of rolling elements using fuzzy entropy and similarity measures
- Wong, M. L. D.; Lee, S. H.; Nandi, A. K.
- 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
- Proceedings of the 10th International Conference on Vibrations in Rotating Machinery, London, United Kingdom, 11-13 September 2012, pp. 693-702
- Publication year
- Anomaly detection; Ball bearings; Fuzzy entropy; Machine condition monitoring; Rolling elements; Rotating elements; Similarity measures
- Woodhead Publishing
- 9780857094520, 0857094521
- Publisher URL
- Copyright © The author(s) and/or their employer(s) unless otherwise stated, 2012.
- Peer reviewed