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New supervision architecture based on on-line modelling of non-stationary data

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Abstract

A new supervision system consisting of three modules is presented. The main novelty is the first module that corresponds to a modelling task. This module, which uses the auto-adaptive and dynamical clustering (AUDyC) neural network, allows us to continuously analyse and classify the functioning state of the monitored system using a dynamical modelling of all known modes (good/bad functioning modes represent different classes). The second module exploits these models of the functioning modes in order to detect “fast” and “slow” deviations. From membership degrees and from the information extracted by the monitoring module, the third module, dedicated to the diagnostics, informs the user about the functioning conditions of the system. In this paper, the main characteristics of the AUDyC and its abilities to model on-line non-stationary data are presented. Then, the description of the supervision system is given and some experimental results stemmed from a supervision application of a hydraulic system are discussed.

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Authors and Affiliations

  1. Laboratoire LAGIS, USTL, Bâtiment P2, 59655, Villeneuve d’Ascq, France

    Stéphane Lecoeuche & Christophe Lurette

  2. Département GIP, Ecole des Mines de Douai, BP838, 59508, Douai, France

    Stéphane Lecoeuche

  3. Laboratoire LME, Université de Valenciennes et du Hainaut Cambrésis, 59304, Valenciennes, France

    Sylvain Lalot

Authors
  1. Stéphane Lecoeuche
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  2. Christophe Lurette
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  3. Sylvain Lalot
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Correspondence to Stéphane Lecoeuche.

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Lecoeuche, S., Lurette, C. & Lalot, S. New supervision architecture based on on-line modelling of non-stationary data. Neural Comput & Applic 13, 323–338 (2004). https://doi.org/10.1007/s00521-004-0427-y

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  • DOI: https://doi.org/10.1007/s00521-004-0427-y

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