摘要
根据系统的特征进行故障诊断往往因为故障的信息不足而难以确诊。将人工智能的思想引入故障诊断的理论之中以克服信息的不足和有效地利用专家知识,是一种解决故障诊断难题的有效方法。结合灰色定性仿真理论和R e iter R的基于第一原理的故障诊断理论的定性定量故障诊断理论被提出,它根据系统的现实状态与用灰色定性仿真预测的系统的状态的差异判断系统是否发生了故障,若故障出现,通过故障状态与故障模型的匹配确定故障的类型。这种方法能有效地结合的定性信息和定量信息,根据系统有限的定量信息建立其变量间的定性约束并将其应用于故障诊断。
It is difficult to diagnose a fault based on the system characters since the information is lacking. To introduce artificial intelligence into fault diagnosis for overcoming the scanty information and utilizing expert knowledge is an effective method for solving the problem. A qualitative - quantitative fault diagnosis theory, which combines Grey Qualitative Simulation （GQSIM） with R Reiter＇s fault diagnosis theory based on the first principle, is advanced in this paper, and it judges whether there exists a fault in a system by the difference between the real state of system and forecasted state by GQSIM. If there exists a fault in the system, the kind of fault is differentiated via matching the states and the models of fault. This method combines effectively the qualitative information with quantitative information, and it can set up the qualitative restrictions among the variables of system according to the limited quantitative information and applies the restrictions to diagnose a fault.
出处
《计算机仿真》
CSCD
2006年第2期146-149,共4页
Computer Simulation
