New approach for systems monitoring based on semi-supervised classification

In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both supervised and unsupervised learning methods. Since supervised learning requires necessarily a broad labelled base that may not always availa...

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Published in:2011 International Conference on Communications, Computing and Control Applications pp. 1 - 6
Main Authors: Theljani, F., Laabidi, K., Lahmari-Ksouri, M., Zidi, S.
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2011
Subjects:
Classification
Computational modeling
Decision-Making
Fault Diagnosis
MLP Network
Monitoring
Optimization
SACA
ISBN:9781424497959, 1424497957
Online Access:Get full text
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Abstract In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both supervised and unsupervised learning methods. Since supervised learning requires necessarily a broad labelled base that may not always available in a sufficient cardinality, we aim at first an unsupervised grouping of a critical faults set (classes) though a Self-Adaptive Clustering Algorithm (SACA). Within this framework, the presented algorithm is based on the evaluation of a metric distance between cluster centroids and samples. An integrated process for optimization allows the tuning of confidence threshold for decision. Next, an additional supervised classification step using Artificial Neural Network (ANN) provides practical information for decision-making. The network is trained according to the classification multi-levels dedicated for multi-class problems. The developed approach is assessed on a hydraulic system consisting of three connected tanks.
AbstractList In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both supervised and unsupervised learning methods. Since supervised learning requires necessarily a broad labelled base that may not always available in a sufficient cardinality, we aim at first an unsupervised grouping of a critical faults set (classes) though a Self-Adaptive Clustering Algorithm (SACA). Within this framework, the presented algorithm is based on the evaluation of a metric distance between cluster centroids and samples. An integrated process for optimization allows the tuning of confidence threshold for decision. Next, an additional supervised classification step using Artificial Neural Network (ANN) provides practical information for decision-making. The network is trained according to the classification multi-levels dedicated for multi-class problems. The developed approach is assessed on a hydraulic system consisting of three connected tanks.
Author Zidi, S.
Theljani, F.
Laabidi, K.
Lahmari-Ksouri, M.
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  givenname: M.
  surname: Lahmari-Ksouri
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  organization: Lab. of Res. Anal. & Control of Syst, Nat. Eng. Sch. of Tunis, Tunis, Tunisia
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  givenname: S.
  surname: Zidi
  fullname: Zidi, S.
  email: Salah_zidi@yahoo.fr
  organization: LAGIS, Univ. des Sci. et Technol. de Lille, Villeneuve d'Ascq, France
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Snippet In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both...
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SubjectTerms Classification
Computational modeling
Decision-Making
Fault Diagnosis
MLP Network
Monitoring
Optimization
SACA
Title New approach for systems monitoring based on semi-supervised classification
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