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An Application of Unsupervised Neural Networks Based Condition Monitoring System

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Artificial Neural Nets and Genetic Algorithms

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Abstract

Integrated condition monitoring for fault identification and maintenance planing is increasingly becoming an indispensable activity in today’s industrial environment. Expert systems and neural networks are emerging to be the latest tools to be applied for condition monitoring. This paper briefly reviews these techniques and describes applications of artificial neural networks in diagnosing the health of various systems.

The application of neural networks discussed here contemplates to devise an intelligent, self-adaptive monitoring module which can be employed in a wider range of industrial environments. The paper describes a general purpose unsupervised neural networks based monitoring system which categorises the operational routines within the individual application environments of a wide range of industrial machinery. The monitor classifies the sensed data into its respective clusters and demonstrate its potential diagnostic capabilities.

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References

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Author information

Authors and Affiliations

  1. Engineering Division, Southampton Institute of Higher Education, Southampton, UK

    M. A. Javed & A. D. Hope

Authors
  1. M. A. Javed
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  2. A. D. Hope
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Editor information

Editors and Affiliations

  1. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  2. Division of Statistics and Operational Research, UK

    Colin R. Reeves

  3. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

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© 1993 Springer-Verlag/Wien

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Javed, M.A., Hope, A.D. (1993). An Application of Unsupervised Neural Networks Based Condition Monitoring System. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_45

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  • DOI: https://doi.org/10.1007/978-3-7091-7533-0_45

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82459-7

  • Online ISBN: 978-3-7091-7533-0

  • eBook Packages: Springer Book Archive

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