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Novelty detection for practical pattern recognition in condition monitoring of multivariate processes: a case study

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Abstract

Multivariate statistical process control (MSPC) can be applied for condition monitoring (CM) purposes. MSPC is implemented using a variety of techniques including neural networks (NNs). In situations when the number of process attributes is sufficiently large (e.g. 10 or more) concerns can arise with respect to training of NNs for pattern recognition. A classification method known as novelty detection (ND) can provide an effective alternative to conventional NN solutions that suffer from the above problem. Despite its great potential, ND is still unknown to the broad community of manufacturing engineers. This paper successfully demonstrates the ability of ND, using Gaussian mixture models, to notify operators of an end-milling process of the presence of faulty tool conditions. A significant achievement is that ND is used to identify abnormal time-series patterns as opposed to individual vectors of multiple simultaneous measurements related to abnormal conditions. Such patterns are found in windowed streams of signals related to 10 different process features (with an effective problem dimension of 140). The paper also investigates some of the issues related to implementation of ND for pattern recognition in condition monitoring .

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Authors and Affiliations

  1. Mechatronics Research Centre, Wolfson School of Mechanical & Manufacturing Engineering, Loughborough University, Leicestershire, LE11 3TU, UK

    F. Zorriassatine, A. Al-Habaibeh & R.M. Parkin

  2. Iensys Ltd., Holywell Building, Holywell Way, Loughborough, LE11 3UZ, UK

    M.R. Jackson

  3. Technology Centre, Royal Mail Group plc, Swindon, SN3 4RD, UK

    J. Coy

Authors
  1. F. Zorriassatine
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  2. A. Al-Habaibeh
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  3. R.M. Parkin
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  4. M.R. Jackson
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  5. J. Coy
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Correspondence to F. Zorriassatine.

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Zorriassatine, F., Al-Habaibeh, A., Parkin, R. et al. Novelty detection for practical pattern recognition in condition monitoring of multivariate processes: a case study. Int J Adv Manuf Technol 25, 954–963 (2005). https://doi.org/10.1007/s00170-004-2174-8

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  • DOI: https://doi.org/10.1007/s00170-004-2174-8

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