WO2017041391A1 - 一级马尔科夫模型开关磁阻电机系统可靠性定量评估方法 - Google Patents

一级马尔科夫模型开关磁阻电机系统可靠性定量评估方法 Download PDF

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WO2017041391A1
WO2017041391A1 PCT/CN2015/099101 CN2015099101W WO2017041391A1 WO 2017041391 A1 WO2017041391 A1 WO 2017041391A1 CN 2015099101 W CN2015099101 W CN 2015099101W WO 2017041391 A1 WO2017041391 A1 WO 2017041391A1
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reluctance motor
motor system
switched reluctance
reliability
order
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PCT/CN2015/099101
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French (fr)
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陈昊
徐帅
董金龙
王星
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中国矿业大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines

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  • the invention relates to a quantitative evaluation method, in particular to a first-order Markov model quantitative evaluation method for reliability of various types and various phase numbers of switched reluctance motor systems.
  • Each phase of the switched reluctance motor system is independently controlled and has good fault tolerance. Characterization of fault-tolerant performance is the key to the establishment and quantitative evaluation of the reliability model of switched reluctance motor systems. Traditional block diagrams and static fault tree modeling ignore the representation of fault-tolerant performance of the system. Qualitative analysis and quantitative calculation based on binary logic The reliability is expected to result in low accuracy and cannot meet the requirements of industrial production. Different faults may occur in different parts of the switched reluctance motor system. After the fault occurs, the excellent fault-tolerant performance ensures that the switched reluctance motor system is in an effective operating state under various fault conditions. However, different faults of different components occur, making the switch reluctance There are too many active operating states in the motor system fault state.
  • the existing Markov reliability modeling method If the existing Markov reliability modeling method is directly adopted, a fault will occupy a Markov space state, causing the state "explosion" problem, and the quantitative estimation of reliability cannot be effectively realized. Moreover, the existing Marco The modeling method of the husband does not realize the hierarchical modeling of the switched reluctance motor system according to the fault type, and the modeling speed is slow. It is urgent to realize quantitative evaluation of the Markov model grading reliability for the switched reluctance motor system.
  • the object of the present invention is to overcome the deficiencies of the prior art, and provide a method for quantitatively analyzing the reliability of a switched reluctance motor system by using a first-order Markov model with simple method, fast evaluation speed, wide application range and wide application range.
  • the first-order Markov model of the present invention quantitatively analyzes the reliability evaluation method of the switched reluctance motor system, and the steps are as follows:
  • the 17 kinds of primary faults that may occur in the switched reluctance motor system are equivalent to 5 Markov states, and the state transition diagram of the switched reluctance motor system under the first-order fault is obtained, and the state transition matrix A under the first-order fault is established. :
  • ⁇ A1 , ⁇ A2 , ⁇ A3 , ⁇ A4 , ⁇ A5 , ⁇ F1 , ⁇ F2 , ⁇ F3 , ⁇ F4 are first-order Markov model state transition rates
  • exp represents an exponential function and t represents time A represents a state transition matrix A;
  • the reliability function R(t) calculates the mean time between failures of the switched reluctance motor system:
  • the first-class Markov model is used to quantitatively analyze the reliability of the switched reluctance motor system.
  • the establishment of the first-level Markov reliability quantitative evaluation model can quickly solve the reliability function and the mean time between failures of the switched reluctance motor system, and is applicable to various structures, multiple phases, and different power levels.
  • Switched reluctance motor system Through the failure mode analysis, the 17 first-order faults that may occur in the switched reluctance motor system are transformed into five states in the Markov space, which solves the state "explosion" problem of Markov reliability modeling; it is well characterized.
  • the intermediate state in which the switched reluctance motor system can operate effectively between normal and fault is consistent with the actual operation of the switched reluctance motor system; under the premise of ensuring the accuracy of the quantitative evaluation of the switched reluctance motor system, the Markov is reduced.
  • the number of spatial states which is fast in calculation and wide in use range, can quickly realize quantitative evaluation of the reliability of the switched reluctance motor system.
  • 1 is a Markov state transition diagram of a switched-resistance motor system of the present invention under a primary fault
  • FIG. 2 is a schematic diagram of a switched reluctance motor system comprising a three-phase 12/8 structure switched reluctance motor and a three-phase double-switching power converter according to the present invention
  • 3 is a reliability function curve obtained by the Markov reliability model of the switched reluctance motor system of the present invention.
  • the switched reluctance motor system can only withstand the occurrence of a first-level fault, and the system fails when the second-level and multi-level faults occur, it is only necessary to establish a first-level Markov reliability quantitative evaluation model.
  • the first-order Markov model has the widest application range. The applicable range of the first-order Markov model is greater than The second-order Markov model, which is also larger than the third-order Markov model, has the fastest calculation speed and the lowest complexity, and is suitable for occasions with fewer equivalent failures and high requirements for failure determination conditions.
  • A1 indicates the effective running state after the capacitor open circuit fault occurs.
  • A2 is the effective running state of the first-stage inter-turn short circuit system
  • the A3 system is out of phase equivalent operating state
  • A4 is the equivalent lower tube short-circuit effective running state
  • A5 is the first level.
  • the system directly enters the failed operating state.
  • the number following the state is the transfer rate of the state after the fault occurs. In actual operation, the operating condition under the primary fault can effectively characterize the fault tolerance of the system.
  • the Markov state transition diagram under the first-order fault of the switched reluctance motor system the Markov space state is represented by a circle, 00 indicates that the system is in normal operation state, F indicates system failure state, and A1 indicates system occurrence.
  • the effective operation state after the capacitor open circuit fault A2 is the effective operation state of the first-stage inter-turn short circuit system, the A3 system is out of phase equivalent operation state, A4 is the equivalent lower tube effective short-circuit operation state, A5 means the system directly enters the failure operation after the first-level fault State, the meaning of the remaining state transfer rate symbols is shown in Table 2:
  • ⁇ DP ⁇ UMO + ⁇ DMO + ⁇ UDS + ⁇ DDS + ⁇ TTO + ⁇ PSO
  • ⁇ DP1 ⁇ DP +0.34( ⁇ DMS + ⁇ PSS )+0.43 ⁇ UMS +0.9 ⁇ TTS
  • the state transition matrix A under the first-order fault is established by the Markov state transition diagram under the first-order fault of the switched reluctance motor system shown in FIG. 1:
  • the first-class Markov model is used to quantitatively analyze the reliability of the switched reluctance motor system.
  • FIG. 1 Another example is a switched reluctance motor system consisting of a three-phase 12/8 structure switched reluctance motor and a three-phase two-switch power converter, as shown in Figure 2, through the switched reluctance motor system shown in Figure 1.
  • the Markov state transition diagram under the first-order fault establishes the state transition matrix A under the first-order fault, and solves the probability matrix P(t) of the switched reluctance motor system in an effective state, and calculates the effective state probability matrix P(t).
  • the sum of the elements obtains the reliability function R(t) of the switched reluctance motor system.
  • the integral of the reliability function curve R(t) in the time domain 0 to infinity can be calculated.
  • the average MTBF of phase-switched reluctance motor system is 424909 Hours, thus achieving a quantitative assessment of the reliability of the first-order Markov model of the three-phase switched reluctance motor system.
  • the mean time between failures reflects the area of the reliability function curve R(t) and the coordinate axis. The larger the area, the more reliable the system.

Abstract

一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估方法，适用于针对开关磁阻电机系统可靠性评估。利用开关磁阻电机系统可能发生的17种一级故障等效为5个马尔科夫状态，得到系统在一级故障下的状态转移图，建立一级故障下的状态转移矩阵，解得开关磁阻电机系统处于有效状态的概率矩阵P(t)，计算有效状态概率矩阵P(t)各元素之和，得到开关磁阻电机系统的可靠度函数R(t)，由可靠度函数R(t)计算出开关磁阻电机系统的平均无故障时间，从而实现了一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估。具有良好的工程应用价值。

Description

一级马尔科夫模型开关磁阻电机系统可靠性定量评估方法 技术领域
本发明涉及一种定量评估方法，尤其适用于各种类型、各种相数的开关磁阻电机系统可靠性的一级马尔科夫模型定量评估方法。
背景技术
开关磁阻电机系统各相独立控制，具有良好的容错性能。容错性能的表征是开关磁阻电机系统可靠性模型建立和定量评估的关键，传统的框图和静态故障树建模，忽略了对系统容错性能的表示，定性分析和基于二值逻辑的定量计算得到的可靠性预计结果，精度较低无法满足工业生产场合的要求。开关磁阻电机系统不同部件可能发生多种故障，故障发生后，优良的容错性能保证开关磁阻电机系统在多种故障状态下处于有效运行状态，然而不同部件不同故障的发生，使开关磁阻电机系统故障状态下的有效运行状态过多。若直接采用现有马尔科夫可靠性建模方法，会使一种故障占用一个马尔科夫空间状态，造成状态“爆炸”的问题，无法有效实现可靠性的定量预计；况且现有的马尔科夫建模方法没有实现对开关磁阻电机系统依据故障类型进行分级建模，建模速度较慢。急需对开关磁阻电机系统实现马尔科夫模型分级可靠性定量评估。
发明内容
本发明的目的是克服已有技术的不足之处，提供一种方法简单，评估速度快，使用范围广，使用范围广的一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估方法。
为实现上述技术问题，本发明的一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估方法，其步骤为：
将开关磁阻电机系统可能发生的17种一级故障等效为5个马尔科夫状态，得到开关磁阻电机系统在一级故障下的状态转移图，建立一级故障下的状态转移矩阵A：
Figure PCTCN2015099101-appb-000001
式中，λA1、λA2、λA3、λA4、λA5、λF1、λF2、λF3、λF4是一级马尔科夫模型状态转移率；
利用公式：
Figure PCTCN2015099101-appb-000002
解得开关磁阻电机系统处于有效状态的概率矩阵P(t)：
Figure PCTCN2015099101-appb-000003
式中，exp表示指数函数，t表示时间A代表状态转移矩阵A；
利用公式(3)计算有效状态概率矩阵P(t)各元素之和，得到开关磁阻电机系统的可靠度函数R(t)：
R(t)＝3.0exp(-3.9773t)-6.427exp(-4.8081t)+0.095445exp(-4.6886t)
+0.40274exp(-4.4988t)+3.9288exp(-4.3777t)  (4)
可靠度函数R(t)计算出开关磁阻电机系统的平均无故障时间：
Figure PCTCN2015099101-appb-000004
从而实现了一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估。
有益效果：一级马尔科夫可靠性定量评估模型的建立，可快速实现对开关磁阻电机系统可靠度函数和平均无故障时间的求解，适用于多种结构、多种相数、不同功率等级的开关磁阻电机系统。通过故障模式分析将开关磁阻电机系统可能发生的17中一级故障转化为马尔科夫空间中5个状态，解决了马尔科夫可靠性建模的状态“爆炸”问题；很好地表征了处于正常和故障之间开关磁阻电机系统能够有效运行的中间状态，与开关磁阻电机系统实际运行情况相符；在保证开关磁阻电机系统可靠性定量评估精度的前提下，减少了马尔科夫空间状态数，其计算速度快，使用范围广，可快速实现对开关磁阻电机系统可靠性的定量评估。
附图说明
图1是本发明的开关磁阻电机系统一级故障下的马尔科夫状态转移图；
图2是本发明的一台由三相12/8结构开关磁阻电机和三相双开关式功率变换器组成的开关磁阻电机系统示意图；
图3是本发明的开关磁阻电机系统马尔科夫可靠性模型解得的可靠度函数曲线。
具体实施方式
下面结合附图中的实施例对本发明作进一步的描述：
若开关磁阻电机系统只能承受一级故障发生，二级及多级故障发生时系统失效，只需建立一级马尔科夫可靠性定量评估模型，一级马尔科夫模型适用范围最广，一级马尔科夫模型的适用范围大于 二级马尔科夫模型，也大于三级马尔科夫模型，计算速度最快，复杂度最低，适用于等效故障数较少和具有失效判别条件高要求的场合。
依据开关磁阻电机系统一级故障下的17种表现形式，得到开关磁阻电机系统在一级故障下的状态转移情况，如表1所示：
表1一级故障下开关磁阻电机系统状态
编号 一级故障类型 系统状态
1 电容开路(CO) A1
2 电容短路(CS) A5
3 下管短路(DMS) A3：0.34 A4：0.54 A5：0.12
4 下管开路(DMO) A3：0.88 A5：0.12
5 上管短路(UMS) A3：0.43 A5：0.57
6 上管开路(UMO) A3：0.88 A5：0.12
7 上二极管短路(UDS) A3：0.88 A5：0.12
8 上二极管开路(UDO) A5
9 下二极管短路(DDS) A3：0.88 A5：0.12
10 下二极管开路(DDO) A5
11 匝间短路(TTS) A2：0.1 A5：0.9
12 极间短路(POS) A5
13 相对地短路(PGS) A5
14 相间短路(PHS) A5
15 匝间开路(TTO) A3：0.88 A5：0.12
16 位置传感器短路(PPS) A3：0.34 A4：0.54 A5：0.12
17 位置传感器开路(PPO) A3：0.88 A5：0.12
表1中A1表示系统发生电容开路故障后有效运行状态，A2是一级匝间短路系统有效运行状态，A3系统缺相等效运行状态，A4是等效下管短路有效运行状态，A5表示一级故障后系统直接进入失效运行状态，状态后面数字为故障发生后进入该状态的转移率，实际运行时，一级故障下的运行情况可实现对系统容错能力的有效表征。
如图1所示，开关磁阻电机系统一级故障下的马尔科夫状态转移图，马尔科夫空间状态用圆圈表示，00表示系统处于正常运行状态，F表示系统失效状态，A1表示系统发生电容开路故障后有效运行状态，A2是一级匝间短路系统有效运行状态，A3系统缺相等效运行状态，A4是等效下管短路有效运行状态，A5表示一级故障后系统直接进入失效运行状态，其余状态转移率符号的含义如表2所示：
表2一级马尔科夫模型状态转移率
Figure PCTCN2015099101-appb-000005
Figure PCTCN2015099101-appb-000006
表2中：
λDP＝λUMO+λDMO+λUDS+λDDS+λTTO+λPSO
λDP1＝λDP+0.34(λDMS+λPSS)+0.43λUMS+0.9λTTS
表2中计算公式用到的符号如表3所示。
表3失效率符号含义
Figure PCTCN2015099101-appb-000007
Figure PCTCN2015099101-appb-000008
由图1所示的开关磁阻电机系统一级故障下的马尔科夫状态转移图，建立一级故障下的状态转移矩阵A:
Figure PCTCN2015099101-appb-000009
利用公式：
Figure PCTCN2015099101-appb-000010
解得开关磁阻电机系统处于有效状态的概率矩阵P(t)：
Figure PCTCN2015099101-appb-000011
式中，exp表示指数函数，t表示时间。
由公式(3)计算有效状态概率矩阵P(t)各元素之和，得到开关磁阻电机系统的可靠度函数R(t)：
R(t)＝3.0exp(-3.9773t)-6.427exp(-4.8081t)+0.095445exp(-4.6886t)
+0.40274exp(-4.4988t)+3.9288exp(-4.3777t)  (4)
由可靠度函数R(t)计算出开关磁阻电机系统的平均无故障时间：
Figure PCTCN2015099101-appb-000012
从而实现了一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估。
又如，对一台由三相12/8结构开关磁阻电机和三相双开关式功率变换器组成的开关磁阻电机系统，如图2所示，通过图1所示开关磁阻电机系统一级故障下的马尔科夫状态转移图，建立一级故障下的状态转移矩阵A，解得开关磁阻电机系统处于有效状态的概率矩阵P(t)，计算有效状态概率矩阵P(t)各元素之和，得到开关磁阻电机系统的可靠度函数R(t)，如图3所示，对可靠度函数曲线R(t)在时间域0到无穷上的积分，可计算出该三相开关磁阻电机系统的平均无故障时间MTTF为424909 小时，从而实现了该三相开关磁阻电机系统一级马尔科夫模型可靠性的定量评估。平均无故障时间反映了可靠度函数曲线R(t)与坐标轴所围面积的大小，面积越大，系统越可靠。

Claims (1)

  1. 一种一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估方法，其特征在于步骤如下：
    将开关磁阻电机系统可能发生的17种一级故障等效为5个马尔科夫状态，得到开关磁阻电机系统在一级故障下的状态转移图，建立一级故障下的状态转移矩阵A：
    Figure PCTCN2015099101-appb-100001
    式中，λA1、λA2、λA3、λA4、λA5、λF1、λF2、λF3、λF4是一级马尔科夫模型状态转移率；
    利用公式：
    Figure PCTCN2015099101-appb-100002
    解得开关磁阻电机系统处于有效状态的概率矩阵P(t)：
    Figure PCTCN2015099101-appb-100003
    式中，exp表示指数函数，t表示时间，A代表状态转移矩阵A；
    利用公式(3)计算有效状态概率矩阵P(t)各元素之和，得到开关磁阻电机系统的可靠度函数R(t)：
    R(t)＝3.0exp(-3.9773t)-6.427exp(-4.8081t)+0.095445exp(-4.6886t)
    +0.40274exp(-4.4988t)+3.9288exp(-4.3777t)   (4)
    可靠度函数R(t)计算出开关磁阻电机系统的平均无故障时间：
    Figure PCTCN2015099101-appb-100004
    从而实现了一级马尔科夫模型定量分析开关磁阻电机系统可靠性评估。
PCT/CN2015/099101 2015-09-11 2015-12-28 一级马尔科夫模型开关磁阻电机系统可靠性定量评估方法 WO2017041391A1 (zh)

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