灰色理论和模糊集结合诊断发动机故障

Combination of grey theory and fuzzy-set in engine fault diagnosis

当民航发动机发生故障时,现有的维修方案一般是通过标准化的流程来解决。但是,标准化的流程并未包含全部的故障解决方案,因此造成了排故效率低下或不能排故的情况出现;另一方面,对经验丰富的工程师而言,同样的故障可能很快找到解决方案。基于案例推理(case-based reasoning,CBR)原理就是利用历史中的源案例,与实际发生的故障进行相似度匹配,以此来模拟经验丰富的工程师判断问题、解决问题的过程,最后参考相似案例给出解决方案。主要研究了灰色理论和模糊集结合的矢量投影学算法,将这种算法用于发动机的故障诊断,达到了很好的效果。

When failures appear in the aviation engine, maintenance programs are now generally implemented through standardized processes. However, standardized processes cannot contain all the failures, which leads to inefficient problem solutions or no solutions. On the other hand, the same problem may be resolved soon for experienced engineers. Case-based reasoning （CBR） means to use a new case to match with practieal application cases, which can imitate the process how those experienced engineers determinate and solve problems. Vector projection learning algorithm combined with grey theory and fuzzy set is studied and is applied for engine fault diagnosis, by which a good result have been achieved.