不完备信息条件下的并发故障诊断方法

在实际的装备故障诊断过程中，经常存在测试信息不完整的情况，而此时对并发故障进行诊断，则更为困难。针对这种情况，首先，对目前应用于不完备信息条件下的故障诊断方法以及并发故障诊断方法进行了分析。定义了不完备并发故障诊断决策系统对存在缺失的测试信息进行表述。提出了不完备边界粗糙熵对决策系统的不确定性进行度量，给出每条测试属性的重要度，同时给出了不完备信息条件下属性值频率的计算方法。然后，为了对并发故障进行诊断，在DSmT框架下构建了并发故障诊断模型，在此模型下提出一种融合证据特征的区间信度合成规则。最后，通过故障诊断实例，验证了方法的有效性和适用性。 

Diagnosis method of simultaneous fault with incomplete information

Test information is often incomplete in the fault diagnosis process of equipment. And simultaneous fault diagnosis process is more difficult at the same time. In response to this situation, the current methods of fault diagnosis with incomplete test information and of simultaneous fault diagnosis are studied firstly. Then we define incomplete fault diagnosis decision system to describe incomplete test information. And it defines incomplete boundary rough entropy to measure the level of uncertainty in the system and assign the importance of each attribute. Meanwhile, the method to calculate frequency of attributes' value under incomplete condition is proposed. In order to diagnose simultaneous fault, the paper constructs the diagnosis model under DSmT framework,and proposes a new combination rule of interval-value belief to overcome the shortcomings of previous methods. Finally, the validity and applicability of the method are proved by two equipment fault diagnosis examples.