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Monitoring of Non Stationary Systems Using Dynamic Pattern Recognition
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Author(s): Laurent Hartert (Université de Reims Champagne-Ardenne, France), Moamar Sayed Mouchaweh (Université de Reims Champagne-Ardenne, France)and Patrice Billaudel (Université de Reims Champagne-Ardenne, France)
Copyright: 2010
Pages: 36
Source title:
Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior
Source Author(s)/Editor(s): Gerasimos Rigatos (Industrial Systems Institute & National Technical University of Athens, Greece)
DOI: 10.4018/978-1-61520-849-4.ch015
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Abstract
The monitoring of non stationary systems permit to follow online the evolutions and changes which occur in the course of time. In Pattern Recognition (PR) the functioning modes are represented by a set of similar patterns, called classes. These patterns are obtained by observation of the most informative parameters of the system. To realize the monitoring of a system functioning PR methods uses a classifier which determines at each instant the class of a new incoming pattern. In this paper, we propose to develop the classification method Incremental Fuzzy Pattern Matching (IFPM) to be operant in the case of dynamic classes and to be used for the online monitoring of evolving systems. IFPM gives good results for static classes and its classification time is constant according to the size of the database. However, with non stationary systems, the classifier parameters must be adapted in order to take into account the temporal changes of classes’ characteristics. These temporal changes can be represented for example by a displacement, a rotation, a splitting, or a fusion of classes. Therefore, the classification method must be able to forget the information which is no more representative of classes and it must adapt its parameters based only on the recent and useful information. This development is based on the use of an incremental algorithm allowing to follow the accumulated gradual changes of classes’ characteristics after the classification of each new pattern. When these changes reach a suitable predefined threshold, the classifier parameters are adapted online using the recent and useful patterns. The developed method is applied on several simulations and on a two tanks benchmark.
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