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Intelligent Technical Fault Condition Diagnostics of Mill Fan

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Novel Applications of Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 586))

Abstract

The mill fans (MF) are centrifugal fans of the simplest type with flat radial blades adapted for simultaneous operation both like fans and also like mills. The key variable that could be used for diagnostic purposes is vibration amplitude of MF corpse. However its mode values include a great deal of randomness. Therefore the application of deterministic dependencies with correcting coefficients is non-effective for MF predictive modeling. Standard statistical and probabilistic (Bayesian) approaches are also inapplicable to estimate MF vibration state due to non-stationarity, non-ergodicity and the significant noise level of the monitored vibrations. Adequate for the case methods of computational intelligence [fuzzy logic, neural networks and more general AI techniques—the precedents’ method or machine learning (ML)] must be used. The present paper describes promising initial results on applying the Case-Based Reasoning (CBR) approach for intelligent diagnostic of the mill fan working capacity using its vibration state.

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Acknowledgements

This work has been partially supported by FP7 grant AComIn № 316087 and partially supported by the National Science Fund of Bulgaria, under the Project No. DVU-10-0267/10.

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Authors and Affiliations

  1. University of Chemical Technology and Metallurgy, Sofia, Bulgaria

    Mincho Hadjiski

  2. Institute of Information and Communication Technologies—BAS, Sofia, Bulgaria

    Lyubka Doukovska

Authors
  1. Mincho Hadjiski
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  2. Lyubka Doukovska
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Corresponding author

Correspondence to Mincho Hadjiski .

Editor information

Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Mincho Hadjiski

  2. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  3. Research & Advanced Engineering, Ford Motor Company, Dearborn, Michigan, USA

    Dimitar Filev

  4. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Vladimir Jotsov

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Hadjiski, M., Doukovska, L. (2016). Intelligent Technical Fault Condition Diagnostics of Mill Fan. In: Hadjiski, M., Kasabov, N., Filev, D., Jotsov, V. (eds) Novel Applications of Intelligent Systems. Studies in Computational Intelligence, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-14194-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-14194-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14193-0

  • Online ISBN: 978-3-319-14194-7

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