基于信息熵的诊断策略优化方法

Diagnostic Strategy Optimization Method Based on Information Entropy Theory

针对现有二值测试诊断策略优化技术无法解决多值测试且测试精度不高的复杂诊断问题,提出多值测试下基于信息熵的诊断策略优化方法。从考虑和不考虑测试过程中可能出现的虚警与漏检2种情况对优化方法进行分析,在2个繁杂程度不同的实例系统上进行仿真验证,并用该算法与AO*算法进行对比。结果表明:该方法在不增高测试费用的同时大大缩短测试时间,结果符合理论预期。

The complex diagnostic problems that the existing binary test diagnostic strategy optimization technology can’t solve the multi-value test and the test accuracy is low,the optimization strategy based on information entropy under multi-value test is proposed.The optimization method is analyzed from considering or non-considering false alarm and missed detection which may occur during the test.The simulation is verified on two different instance systems with different degrees of complexity,and the algorithm is compared with the AO*algorithm.The results show that the method has the characteristics of not increasing the test cost and greatly reducing the test time.The result is in line with the theoretical expectation.