摘要
本文提出了一种神经网络和模糊系统相结合的分级式故障诊断方法。神经网络通过对部分测量数据的处理,实现系统的回路级故障诊断,输出各回路故障出现的可信度。模糊系统通过对神经网络得到的初步诊断结果和其他测量值的处理,实现系统的元件级故障诊断,并对最终诊断结果作出解释。该方法融合了神经网络自适应学习能力强和模糊系统知识表达明确的优点,简化了神经网络学习数据获取及模糊推理规则建立的过程。通过对热硝酸冷却系统故障诊断的仿真,证明了该故障诊断方法的有效性。
In this paper, an artificial neural network (ANN) is integrated with fuzzy system for fault diagnosis. ANN detects the loop faults sources through the partial data measured, and outputs the fault degrees of corresponding loops. Fuzzy system detects the element faults through the preliminary diagnosis results obtained by ANN connected with other correlative values measured, and interprets the final results. The fault diagnosis system(FDS) combines the adaptive learning diagnosis procedure of the ANN and the transparent knowledge representation of the fuzzy system, and simplifies the process of obtaining the ANN's learning sample and establishment the fuzzy inference rules. Through the fault diagnosis simulation of a hot nitric acid cooling system, it has been proven that the fault diagnosis method is very valid.
出处
《计算技术与自动化》
2004年第2期31-34,47,共5页
Computing Technology and Automation
