Method for quantitatively evaluating reliability of switched reluctance machine system through two-level Markov model

Abstract

The invention provides a method for quantitatively evaluating reliability of a switched reluctance machine system through a two-level Markov model. 17 kinds of primary faults probably occurring in the normal state of the switched reluctance machine system are equivalent to 4 effective states and one ineffective state of Markov space under the primary faults, the system expression form under secondary faults is equivalent to 14 effective states and 4 ineffective states based on the 4 effective states, and meanwhile 19 effective states and 6 ineffective states under the two levels of faults of the system are obtained in consideration of the initial normal state and the final ineffective state; a state transition diagram of the system under the two levels of faults is established, an effective state transition matrix under the two levels of faults is obtained, the sum of all elements of the effective state probability matrix is calculated, a reliability function of the switched reluctance machine system is obtained, the mean free error time of the switched reluctance machine system is calculated, and therefore the reliability of the switched reluctance machine system is evaluated through quantitative analysis through the two-level Markov model. High engineering application value is achieved.

Description

technical field [0001] The invention relates to a quantitative evaluation method, which is especially applicable to the second-order Markov model quantitative analysis method for evaluating the system reliability of switched reluctance motors of various types and phases. Background technique [0002] The switched reluctance motor has a solid structure, no winding on the rotor, small phase-to-phase coupling, and excellent fault tolerance. Good fault-tolerant performance ensures high reliability of the switched reluctance motor system. However, the existing reliability quantitative evaluation methods cannot effectively characterize the fault tolerance of the system and cannot meet the requirements of industrial applications. Reliability block diagram modeling and fault tree modeling ignore the fault tolerance of the switched reluctance motor system, and cannot represent the effective operating state of the switched reluctance motor system between the normal state and the fail...

AI Technical Summary

Problems solved by technology

However, the existing quantitative reliability evaluation methods cannot effectively characterize the fault tolerance of the system and cannot meet the requirements of industrial applications.

Reliability block diagram modeling and fault tree modeling ignore the fault tolerance of the switched reluctance motor system, and cannot represent the effective operating state of the switched reluctance motor system between the normal state and the failure state. Although the model can represent the intermediate state between normal and failure, the conventional Markov modeling method only considers multiple faults of the switched reluctance motor system to be equivalent to a Markov state, and does not consider a fault Can enter different Markov states, which will produce wrong quantitative evaluation results of reliability

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