基于贝叶斯网络Noisy Or模型的水电机组故障诊断研究

Study of hydropower units fault diagnosis based on Bayesian network Noisy Or model

针对水电机组故障诊断中系统结构复杂,不确定因素众多的特点,将贝叶斯网络引入水电机组故障诊断中,建立基于贝叶斯网络的水电机组故障诊断系统;为了克服贝叶斯网络结构中需要的概率数量庞大和确定概率困难特点,在贝叶斯网络系统结构中引入Noisy Or模型。论文首先利用专家知识,将各个特征结点按二值结点构造网络,确定各单个结点的概率,然后计算多个结点的任意组合对结果的影响程度,从而确定某种故障发生的可能程度。仿真研究表明:用此模型构造的贝叶斯网络结构,需要的条件概率个数可以从2n减小为2n。大大降低了数据需求量,提高了水电机组故障诊断速度和效率。

A fault diagnosis system of hydropower units is developed using a Bayesian network to consider complicated system structure and uncertain factors in fault diagnosis. This system adopts a Noisy Or model to overcome the shortcomings of the network in large numbers of probabilities and difficulty in determining their values. Taking the characteristic nodes as binary nodes, it uses experts＇ knowledge determining the probability at each node and calculates the influence of random combinations of multiple nodes on the outcome, thus determining the possibility of occurrence of a certain fault. The simulations indicate that the number of conditional probabilities required by this new model is decreased from 2n to 2n, and thus it significantly reduces the data demand and raises the time efficiency of hydropower units fault diagnosis.