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Intelligent Technical Fault Condition Diagnostics of Mill Fan

  • Mincho Hadjiski
  • Lyubka Doukovska
Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Vibration Amplitude Steam Generator Thermal Power Plant Diagnostic State Vibration State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Chemical Technology and MetallurgySofiaBulgaria
  2. 2.Institute of Information and Communication Technologies—BASSofiaBulgaria

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