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Robust clustering of acoustic emission signals using the Kohonen network

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ICASSP '00: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06

Pages 3891 - 3894

https://doi.org/10.1109/ICASSP.2000.860253

Published: 05 June 2000 Publication History

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Abstract

Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.

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Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys (CSUR)10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Prokopenko MWang PScott AGerasimov VHoschke NPrice D(2005)On self-organising diagnostics in impact sensing networksProceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV10.1007/11554028_25(170-178)Online publication date: 14-Sep-2005
https://dl.acm.org/doi/10.1007/11554028_25
Markou MSingh S(2003)Novelty detectionSignal Processing10.1016/j.sigpro.2003.07.01983:12(2499-2521)Online publication date: 1-Dec-2003
https://dl.acm.org/doi/10.1016/j.sigpro.2003.07.019

Index Terms

Robust clustering of acoustic emission signals using the Kohonen network
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

Index terms have been assigned to the content through auto-classification.

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ICASSP '00: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06

June 2000

694 pages

ISBN:0780362934

Publisher

IEEE Computer Society

United States

Publication History

Published: 05 June 2000

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Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys (CSUR)10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Prokopenko MWang PScott AGerasimov VHoschke NPrice D(2005)On self-organising diagnostics in impact sensing networksProceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV10.1007/11554028_25(170-178)Online publication date: 14-Sep-2005
https://dl.acm.org/doi/10.1007/11554028_25
Markou MSingh S(2003)Novelty detectionSignal Processing10.1016/j.sigpro.2003.07.01983:12(2499-2521)Online publication date: 1-Dec-2003
https://dl.acm.org/doi/10.1016/j.sigpro.2003.07.019

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Study on Spectrum Characteristics and Clustering of Acoustic Emission Signals from Rock Fracture

A modified Kohonen map algorithm for clustering time series data

Acoustic Field Analysis of Surface Acoustic Wave Dispersive Delay Lines Using Inclined Chirp IDT

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