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Home List of Titles Modified self-organising map for automated novelty detection applied to vibration signal monitoring
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/5513
- Modified self-organising map for automated novelty detection applied to vibration signal monitoring
- Wong, M. L. D.; Jack, Lindsay B.; Nandi, Asoke Kumar
- This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.
- Publication type
- Journal article
- Research centre
- Swinburne University of Technology. Sarawak School of Engineering
- Mechanical Systems and Signal Processing, Vol. 20, no. 3 (2006), p. 593-610
- Publication year
- Artificial neural network; Condition monitoring; Features extraction; Higher-order statistics; Novelty detection; Self-organising map; Vibration signal processing
- Publisher URL
- Copyright © 2005 Elsevier Ltd. All rights reserved.
- Peer reviewed