摘要
针对发电厂制粉系统故障与征兆对应关系复杂及过程信息的不确定性及传统BP神经网络故障诊断的缺点,提出了基于粗糙集概率神经网络(RSPNN)的制粉系统故障诊断方法,以改善传统BP神经网络初始值敏感、易使学习过程陷入局部极小值以及样本数据过大时训练速度慢等问题。首先采用自组织映射神经网络(SOMNN)对连续样本数据进行离散化;再利用基于区分矩阵的HORAFA算法对离散化样本数据进行RS属性约简,并将约简结果作为概率神经网络(PNN)的输入;最后利用PNN作为诊断决策分类器,输出故障模式,并进行了仿真研究。仿真结果表明,该方法不仅优化神经网络的拓扑结构,降低神经网络的训练时间,而且能准确、快速地诊断制粉系统故障类型,同时对发电厂制粉系统及其相关设备的在线故障诊断问题有一定启发性。
Due to the complicated relationship between the faults and corresponding symptoms of pulverizing systems, uncertainty of information, and the shortcomings of the general BP learning algorithm is training neural networks, a fault diagnosis system based on rough sets probabilistic neural networks （RSPNN） is proposed to deal with the traditional problems appearing in fault diagnosis techniques such as the sensitive initial value, the learning process into a local minimum and the slow training. Firstly, continuous attributes are quantized by SOM. Secondly, a HORAFA method based on distinguish matrix is used in the heuristic reduction of RS to reduce the samples as the input of the probabilistic neural networks （PNN）. Then, the PNN is used as a classifier to predict fault. The simulation results show that the method optimizes the structure of neural network, decreases the computation complexity, improves the diagnosis correctness, and provides inspiration about on-line fault diagnosis for pulverizing system and related equipment.
出处
《控制工程》
CSCD
北大核心
2012年第3期412-415,共4页
Control Engineering of China
基金
国家自然科学基金(No.60804017
60835001
60904020
60974120)
教育部博士点基金(No.20070286039
20070286001)
关键词
制粉系统
故障诊断
粗糙集
概率神经网络
pulverizing system
fault diagnosis
rough sets
probabilistic neural network
