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Computational Intelligence in Fault Diagnosis pp 1–36Cite as

Computational Intelligence Methodologies in Fault Diagnosis: Review and State of the Art

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

This first chapter of the book introduces the reader to the area of computational intelligence techniques and to their significant and abundant applications to fault diagnosis. Fault diagnosis represents an important contemporary research field, due to the ever-increasing need for safety, maintainability and reliability of industrial plants. The research in this field influences important areas of our day-to-day life by increasing security when using safety-critical devices, extending the lifetime of many expensive devices, and improving efficiency of manufacturing lines, which leads to smaller production expenses and lower prices for the end user.

The main problems raised by the processes taking place within modern industrial plants are their high nonlinearity, noisy signals, and uncertainty. Computational intelligence techniques — neural networks, fuzzy techniques, genetic algorithms, etc. — are the very answer of the fault diagnosis research community to these problems. This book represents a collection of recent results on applying various computational intelligence techniques to fault diagnosis. In this introductory chapter, the reader is presented with a short description of the main computational intelligence techniques together with a literature review on their applications to fault diagnosis.

Another major problem raised by the modern industrial plants is their high level of complexity. The complexity of a plant is understood here as the impossibility to model its global emergent behavior using state-of-the-art modeling techniques. Unfortunately, even if they offer better performance than mathematical models when modeling processes with reasonable complexity, the computational intelligence techniques cannot successfully model very complex processes.

The answer given by the research community to this problem is to develop distributed fault diagnosis methodologies. The main idea is to partition the monitored system in subsystems having a reasonable complexity level and, then, to successfully apply state-of-the-art methodologies on each one of them. The global diagnosis of the system is going to be based on all these local diagnosis processes. Implementing the local diagnosis processes using computational intelligence methodologies retains their ability to treat the local nonlinearities, noise and uncertainty. The book contains a special chapter dealing with distributed fault diagnosis methodologies.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, “Dunarea de Jos” University of Galati, Domneasca 47, Galati, Romania

    Cosmin Danut Bocaniala

  2. Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK

    Vasile Palade

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  1. Cosmin Danut Bocaniala
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  2. Vasile Palade
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Editors and Affiliations

  1. Oxford University Computing Laboratory, Oxford, UK

    Vasile Palade PhD

  2. KES Center, University of South Australia, Australia

    Lakhmi Jain PhD

  3. Department of Communication Systems, Lancaster University, Lancaster, UK

    Cosmin Danut Bocaniala Phd

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Bocaniala, C.D., Palade, V. (2006). Computational Intelligence Methodologies in Fault Diagnosis: Review and State of the Art. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_1

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  • DOI: https://doi.org/10.1007/978-1-84628-631-5_1

  • Publisher Name: Springer, London

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  • Online ISBN: 978-1-84628-631-5

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