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Hidden Markov models in task of condition monitoring 743
This paper describes hidden Markov models use in technical objects condition monitoring. A hidden Markov model is a statistical model in which it is assumed that a system is Markov process with unknown parame¬ters. Based on observations generated by the system, model parameters are extracted. Models described by these parameters are used for further analysis and prediction studies. The paper discusses relationships and differences between a visible Markov model and its hidden counterpart. In considered task hidden Markov models are used to detect changes in the technical state of examined object. The main problem of method developed is to elaborate method which will be able to detect significant changes in a system observation vector and ascribe a given observation to the present technical state. An example of using hidden Markov models for rolling bearings is presented.
Modern research is often aimed at building such a diagnostic system which will allow to determine the technical state of tested object. Correct and certain determination of technical state is important in condition monitoring and makes it possible to determine future operating strategies for minimizing operating costs and failure hazards. Change of technical state is usually connected with changes in the object kinematics, development of failures, changes in cooperated kinematics pairs.
Статьи по теме
Теория и методология | Куравский Л.С., Баранов С.Н., Юрьев Г.А.