一种实现灰箱系统故障定位的诊断技术

故障定位是故障诊断的关键之一，而神经网络 的训练和学习速度问题，一般不适用于通过在线学习而实现的动态诊断．本文提出一种以对 象可知部分数学模型为基础，结合输出补偿构造神经网络，建造基于神经网络状态观测器的 深知识诊断方法，实现故障隔离和部件诊断功能．网络补偿部分的联接权采用在线学习方法 ，具有快速的收敛速度和良好的跟踪特性．

A FAULT POSITIONING DIAGNOSIS TECHNIQUE FOR GREY BOX SYSTEM

Because of the learning and training speed, the neural networks is not used in dynamic diagnosis with online selflearning in general. And the fault positioning is very important. In this paper, an observer method, is put forward to realize functions of fault isolation and part diagnosis, which is based on neural networks, built on mathematical model for the known part of the object, and combined with compensation net and the fault positioning technique. It is a deep knowledge method with fast convergence speed and good tracking character istics when training process is online.