基于改进EMD与PNN的汽轮机通流部分故障诊断

根据热力参数非线性、非稳态的特点,提出了一种基于改进的经验模态分解(empirical mode decomposition,简称EMD)算法与概率神经网络(probabilistic neural network,简称PNN)的汽轮机通流部分故障诊断新方法。该方法针对EMD存在的端点效应问题,采取基于波形相似度的镜像延拓法进行改进,以得到更准确、更真实的本征模函数(intrinsic mode function,简称IMF)分量,从而有效提取了故障特征信息,并通过PNN训练判别汽轮机通流部分故障类型。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,基于改进EMD与PNN的汽轮机通流部分诊断方法能够快速准确地判别汽轮机通流部分的故障类型,其准确率明显高于基于EMD与PNN的故障诊断方法。

Research on Fault Diagnosis of Steam Turbine Flow Passage Based on Improved EMD and PNN

According to the non-linear and non-stationary characteristics of thermodynamic parameter parameters, this paper proposes a new fault diagnosis method for steam turbine flow passage (STFP) based on improved empirical mode decomposition (EMD) and probability neural network (PNN). In view of the end effect in the conventional EMD, an improved EMD is proposed to get more reliable results of intrinsic mode functions (IMF) by using mirror extension based on waveform similarity. Then, it is applied to decompose the thermal parameter signals to obtain a series of stationary IMF and a residual, through which the feature extraction of STFP fault is realized effectively. Finally, the feature vectors are inputted into the PNN to recognize the fault patterns. Simulation experiments are carried out based on the actual operation data of a 600MW thermal power plant unit. The results verify that: the proposed fault diagnosis method can quickly and accurately identify the fault patterns of STFP, and it has better performance than STFP fault diagnosis method based on conventional EMD-PNN.