基于概率Petri网的故障诊断模型研究

Research of fault diagnostic model based on probability Petri nets

为解决不确定条件下的故障诊断问题,在传统Petri网基础上,引入概率理论,提出了概率Petri网的概念;概率Petri网将事件发生的概率及其逻辑推理过程引入Petri网的设计及矩阵运算中,以反映事件转换过程中发生的可能性。针对概率Petri网特点,故障诊断模型的设计建立在根据简化样本集获取的诊断规则基础上,以避免复杂系统建模时出现的组合爆炸问题,诊断规则的获取可以有效推广故障诊断范围,使其不仅仅局限于样本集;同时,针对诊断规则的形式,定义并提出将诊断规则转换为标准基本规则序列,便于模型的程序化设计。通过旋转机械故障诊断的示例证明了这种方法的可行性与有效性。

In order to solve the influence of uncertainty about information in fault diagnosis.The concept about probability Petri nets is defined in this paper.The probability Petri nets can reflect the probability of the transition of a process and the consequence of the logic can be calculated by the incidence matrix.The design of the fault diagnostic model is based on the reduced swatch set to abate the complexity of the Petri nets.Because the complicated Petri nets will induce the combinatorial states expand quickly.And in order to get the logic between condition attributes and the results.The method about the generation of the default diagnostic rules is used to acquire the diagnostic rules.The rules can also extend the range of the diagnosis not only for the swatch set.For the programmable design of the model,the basic format rule is defined and all diagnostic rules can be transformed to a series of basic rule.The design is proved to be availability by the example about rotating machinery fault diagnosis in this paper.