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Advances of Computational Intelligence in Industrial Systems pp 269–296Cite as

Automated Novelty Detection in Industrial Systems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 116))

Novelty detection is the identification of abnormal system behaviour, in which a model of normality is constructed, with deviations from the model identified as “abnormal”. Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Given the complexity of such systems, the number of possible failure modes is large, many of which may not be characterised sufficiently to construct a. traditional multi-class classifier [22]. Thus, novelty detection is particularly suited to such cases, which allows previously-unseen or poorly-understood modes of failure to be correctly identified.

This chapter describes recent advances in the application of novelty detection techniques to the analysis of data from gas-turbine engines.Whole-engine vibration-based analysis will be illustrated, using data measured from casemounted sensors, followed by the application of similar techniques to the combustor component. In each case, the investigation described by this chapter shows how advances in prognostic condition monitoring are being made possible in a principled manner using novelty detection techniques.

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Author information

Authors and Affiliations

  1. Department of Engineering Science, University of Oxford, Oxford, UK

    David A. Clifton, Peter R. Bannister & Lionel Tarassenko

  2. Oxford BioSignals Ltd., Abingdon, Oxfordshire, UK

    David A. Clifton

  3. Nuffield Department of Anaesthetics, University of Oxford, Oxford, UK

    Lei A. Clifton

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  1. David A. Clifton
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  2. Lei A. Clifton
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  3. Peter R. Bannister
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  4. Lionel Tarassenko
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Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Ying Liu

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798

    Aixin Sun  & Ee-Peng Lim  & 

  3. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576

    Han Tong Loh  & Wen Feng Lu  & 

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Clifton, D.A., Clifton, L.A., Bannister, P.R., Tarassenko, L. (2008). Automated Novelty Detection in Industrial Systems. In: Liu, Y., Sun, A., Loh, H.T., Lu, W.F., Lim, EP. (eds) Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78297-1_13

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  • DOI: https://doi.org/10.1007/978-3-540-78297-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78296-4

  • Online ISBN: 978-3-540-78297-1

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