融合阴阳补偿理论的软计算故障诊断方法

应用软件计算方法，融合模糊理论，神经网络和遗传算法的优点，结合阴阳补偿理论设计一种用于柴油机振动故障诊断的新方法，该方法补了单独使用模糊方法或人工神经网络方法存在的规则数多和收敛速度慢等不足，其中推理系统采用的是模糊神经网络知识发现系统FNNKD（fuzzy neural network with knowledge discovery)，应用该系统虽然花费的训练时间较长，但通过训练得到的系统参数可以达到全局最优，应用时，识别精度高而计算复杂度较低，通过CA10B汽车变速箱齿轮和轴承故障振动试验验证了该方法的可行性。

Soft Computation Fault Diagnosis Method Associated with the Yinyang Compensatory Theory

A novel method used in the fault diagnosis for diesel engines is presented. The method is based on soft computing theory that fuses together fuzzy theory, neural network and genetic algorithm, and combines itself with the Yinyang compensatory theory. In this way, shortcomings such as an excessive multiplicity of rules, or slow convergence when employing the fuzzy logic or neural network separately can be overcome. The inference system selected here is FNNKD (fuzzy neural network with knowledge discovery). Although the use of this system takes a longer training time, the parameter obtained after training are optimal in a global field. When using this system, accuracy in recognition will increase and the complexity in calculation will decrease. Finally, its feasibility is verified in the experimental study of vibration in gear box and bearing faults of a CA10B automobile.