Three-level Markov model switch magnetic resistance motor system reliability quantitative evaluation method

Abstract

The invention relates to a three-level Markov model switch magnetic resistance motor system reliability quantitative evaluation method. According to the method, system operation states of a switch magnetic resistance motor-driven system in a first-level fault, a second-level fault and a third-level fault are analyzed to acquire that 4 valid states and 1 invalid state exist in the first-level fault, 14 valid states and 4 invalid states exist in the second-level fault, 43 valid states and 14 invalid states exist in the third-level fault, the initial normal state and a final invalid state are further considered, so 62 valid states and 20 invalid states exist in a three-level Markov model, a state transfer graph of the switch magnetic resistance motor system in the third-level faults is established, a state transfer matrix is acquired, a probability matrix P(t) of the system in the valid states can be acquired through operation, sum of elements of the valid state probability matrix P(t) is calculated, average faultless time is calculated through the reliability function R(t), and so reliability evaluation of the three-level Markov model quantitative analysis switch magnetic resistance motor system is realized. The method has great engineering application values.

Description

technical field [0001] The invention relates to a quantitative evaluation method, in particular to a three-stage Markov model quantitative evaluation method applicable to the system reliability of switched reluctance motors of various types and phase numbers. Background technique [0002] Quantitative reliability evaluation mainly includes two parts: establishment of reliability model and quantitative solution based on reliability model. The traditional reliability modeling method can only represent the basic normal state and failure state of the switched reluctance motor system, and cannot realize the characterization of all operating states of the entire operating cycle of the switched reluctance motor system. Although the dynamic fault tree and the Markov model can represent all possible states of the system, the establishment of the dynamic fault tree model requires complex theoretical analysis and is not conducive to subsequent quantitative solutions. Now the commonly ...

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Problems solved by technology

The Boolean logic method and Bayesian method cannot meet the analysis requirements in the case of multiple components and multiple faults, while the conventional Markov state space method can solve the above problems, but the solution time is too long due to the influence of the number of space states , cannot meet the requirement of fast reliability modeling

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