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针对民航飞机系统故障诊断的复杂性以及民航对快捷性和安全性的要求,采用了基于优先权值的模糊Petri网(PFPN)的故障诊断模型对某型号飞机空调系统进行故障诊断,以提高诊断过程的快速性和准确性。利用概率统计分析数据得到模糊产生式规则并建立模糊petri网,根据正反向推理相结合的算法,在反向推理中引入表示故障严重程度的优先权值,查找故障路径,用正向推理计算故障发生的可信度,得到故障起始原因,并检验反向推理得到的路径有效性。实验结果表明,增加的优先权值参数解决了当同一级别的几个变迁的可信度相差不大甚至相等时,仅依据可信度的最大的原则进行取舍的不合理性的问题,并且反正向相结合的推理算法提高了推理搜索速度,验证了算法的有效性。
Aiming at the complexity of civil aviation aircraft system fault diagnosis and the requirements of civil aviation for fastness and safety, a fault diagnosis model of fuzzy Petri nets (PFPN) based on priority value is adopted to diagnose a certain type of aircraft air conditioning system to improve diagnosis The speed and accuracy of the process. Based on the data of probability and statistics, the rules of fuzzy production are obtained and the fuzzy petri nets are established. According to the combination of forward and reverse reasoning, the priority value is introduced into reverse reasoning to find the fault path, and the fault path is calculated by forward reasoning The credibility of the fault occurs, the cause of the fault is obtained, and the validity of the path obtained by reverse reasoning is tested. The experimental results show that the increased priority value parameter solves the unreasonable problem of selecting and relying only on the maximum principle of credibility when the confidences of several transitions at the same level differ little or not, The combination of reasoning algorithm to improve the speed of inference search to verify the effectiveness of the algorithm.