Modified self-organising map for automated novelty detection applied to vibration signal monitoring

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/5513

Title: Modified self-organising map for automated novelty detection applied to vibration signal monitoring
Author(s): Wong, M. L. D.; Jack, Lindsay B.; Nandi, Asoke Kumar
Abstract: 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
Source: Mechanical Systems and Signal Processing, Vol. 20, no. 3 (2006), p. 593-610
Publication year: 2006
Keyword(s): Artificial neural network; Condition monitoring; Features extraction; Higher-order statistics; Novelty detection; Self-organising map; Vibration signal processing
Publisher: Elsevier
Format: 593-610
ISSN: 0888-3270
Publisher URL: http://dx.doi.org/10.1016/j.ymssp.2005.01.008
Peer reviewed