实例推理中遗传训练算法用于机械失效模式识别的研究

采用实例推理和遗传算法相结合的方法，研究了航空机械零部件失效模式识别的问题。对用于识别的失效属性的选择、检索相似度计算、训练用遗传算法的适应度函数设计以及训练策略的影响进行了较为详细的描述。应用测试表明，对包含分布均衡的3种模式的情况取得了高于74．67％的识别率，所获得的最佳权值向量对另外2种模式具有很好的识别精度（大于73．3％），对混合多模式情况也具有较好的推广能力。验证了该方法对航空零部件失效模式的识别是可行的。

Research of Genetic Training Algorithm for Identifying Mechanical Failure Modes within the Framework of Case-Based Reasoning

The combination of case-based reasoning (CBR) and genetic algorithm (GA) is considered in the problem of failure mode identification in aeronautical component failure analysis. Several implementation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74 67% can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73 3% of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.