摘要
针对火电机组热力参数动态数据的海量、高维特点,提出了一种基于动态数据挖掘的热力参数传感器故障诊断新方法。该方法通过对热力参数信号进行经验模态分解,获得一系列平稳的本征模态函数(intrinsic mode function,简称IMF)分量和一个趋势余量,实现传感器故障特征信息的动态挖掘。以各IMF分量和趋势余量的方差作为特征向量构建欧氏距离判别函数,结合径向基函数神经网络确认传感器是否发生故障。根据专家经验得到的规则分析传感器测量值与理论值之间的差值,判别传感器的故障类型。以某电厂600MW火电机组实时运行数据为基础进行仿真实验,结果表明:该方法能够仅使用热力参数传感器正常状态下的样本,有效区分传感器故障造成的信号变化与机组本身正常负荷波动造成的信号变化,实现快速准确地对热力参数传感器的工作状态和故障类型进行判别。
According to the massive and high-dimensional characters of the thermal parameter dynamic data derived from thermal power plants,a new fault diagnostic method for the thermal parameter sensor wasproposed that was based on dynamic data mining.First,thermal parameter signals were decomposed into a series of intrinsic mode functions(IMFs)and a residual,through which the dynamic mining of sensor fault feature was effectively realized.Second,the variances of IMFs and the residual were proposed as eigenvectors for creating a Euclidean distance criterion function,and then a radical basis function(RBF)neural network was used to verify whether the sensor was at fault or not.Finally,based on expert experiences,the difference value between the measured and calculated values of the sensor were analyzed to classify the fault patterns.A simulative experiment was carried out based on the actual operation data of a 600 MW thermal power plant unit.The calculation results verified that the proposed method can distinguish a signal change caused by a sensor fault from normal process dynamics only with the samples of the thermal parameter sensor in a normal situation,and can identify the working condition and fault patterns of the thermal parameter sensor quickly and accurately.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2016年第4期694-699,809-810,共6页
Journal of Vibration,Measurement & Diagnosis
基金
中央高校基本科研业务费专项资金资助项目
关键词
热力参数传感器
动态数据挖掘
经验模态分解
径向基神经网络
故障诊断
thermal parameter sensor
dynamic data mining
empirical mode decomposition
radical basis function neural network
fault diagnosis
