基于模型和案例推理的混合故障诊断方法

基于模型的诊断方法描述的是系统内部结构和行为的深知识，能够轻松获取诊断知识，但计算量较大；而基于案例推理的诊断方法依靠的是历史经验，诊断效率比较高，但知识获取是其瓶颈。针对两种方法的优缺点，提出了利用基于模型的方法来实现案例知识的获取，然后通过基于案例推理的方式完成故障诊断过程的混合故障诊断方法。该方法不仅汲取了基于模型方法准确度高的优点，避免了知识获取难的问题，同时继承了基于案例推理方法的快速性，提高了诊断效率。

Hybrid method for fault diagnosis based on model and case-based reasoning

Model-based diagnosis which describes the deep knowledge of internal structure and behavior of a system can easily obtain diagnostic knowledge, while its disadvantage is the large amount of calculation. The diagnostic efficiency of case-based reasoning which relies on historical experience is relatively high, but knowledge acquisition is its bottleneck. Therefore, this paper presents a reasoning method combining model-based diagnosis with case-based reasoning to improve the practicability of predicate logic model based diagnosis. The process of case acquisition is achieved by model-based diagnosis and the fault diagnosis result is given through case-based reasoning. This approach overcomes the problem of knowledge acquisition and inherits the high-speed characteristics of case based reasoning.