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Automatic Control and Computer Sciences

, Volume 53, Issue 6, pp 532–549 | Cite as

Intelligent Constructing Exact Statistical Prediction and Tolerance Limits on Future Random Quantities for Prognostics and Health Management of Complex Systems

  • N. A. NechvalEmail author
  • G. Berzins
  • K. N. Nechval
Article

Abstract

In the paper presented a novel technique of intelligent constructing exact statistical prediction and tolerance limits on future random quantities for prognostics and health management of complex systems under parametric uncertainty is proposed. The aim of this technique is to develop and publish original scientific contributions and industrial applications dealing with the topics covered by Prognostics and Health Management (PHM) of complex systems. PHM is a set of means, approaches, methods and tools that allows monitoring and tracking the health state of a system in order to detect, diagnose and predict its failures. This information is then exploited to take appropriate decisions to increase the system’s availability, reliability and security while reducing its maintenance costs. The proposed technique allows one to construct developments and results in the areas of condition monitoring, fault detection, fault diagnostics, fault prognostics and decision support.

Keywords:

future random quantities parametric uncertainty exact prediction and tolerance limits pivotal quantity averaging approach prognostics and health management of complex systems 

Notes

ACKNOWLEDGMENTS

The authors would like to thank the anonymous reviewers for their valuable comments that helped to improve the presentation of this paper.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest regarding the publication of this paper.

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Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.BVEF Research Institute, University of LatviaRigaLatvia
  2. 2.Transport and Telecommunication InstituteRigaLatvia

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