基于小波包-LSTM神经网络磨煤机故障诊断  

Fault diagnosis of coal mill based on wavelet packet-LSTM neural network

作　　者：陈波 徐文韬 黄亚继[2] 曹歌瀚 李雨欣 管诗骈[1] 王亚欧[1] CHEN Bo;XU Wentao;HUANG Yaji;CAO Gehan;LI Yuxin;GUAN Shipian;WANG Ya′ou(Jiangsu Frontier Electric Technology Co.,Ltd.,Nanjing211102,China;Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education,Southeast University,Nanjing210096,China)

机构地区：[1]江苏方天电力技术有限公司,南京211102 [2]东南大学能源热转换及其过程测控教育部重点实验室,江苏南京210096

出　　处：《洁净煤技术》2022年第5期211-220,共10页Clean Coal Technology

基　　金：江苏方天电力技术有限公司科技资助项目(KJ201927);国家重点研发计划资助项目(2018YFC1901205)。

摘　　要：磨煤机作为火电厂制粉系统最重要的设备,设备运行状态直接影响火电机组运行性能,因此,对磨煤机进行故障诊断对于保障电厂安全生产具有实际意义。针对磨煤机实际运行过程中故障类型难以确定及故障诊断时间滞后等问题,提出一种基于小波包-LSTM神经网络磨煤机故障诊断方法。首先,基于LSTM神经网络建立磨煤机出口压力和出口温度预测模型,以亚临界燃煤机组为例,将磨煤机运行时正常数据与故障数据组成混合数据作为LSTM神经网络预测模型输入量,预测磨煤机出口压力和出口温度并获取残差信号;其次,针对残差信号进行小波包分解,辨识残差信号突发奇异点;最后,对磨煤机故障库中故障发生前后部分数据和混合数据中故障发生前后同时段内部分数据的全部变量变化趋势进行相关程度分析以诊断磨煤机故障类型。结果表明:LSTM神经网络预测磨煤机出口压力和出口温度最大相对误差不超过1%;采用小波包分解残差信号能够较精确地确认故障发生时刻;采用相关系数法分析全部变量变化趋势能辨识磨煤机故障类型。As the most important equipment in pulverizing system of thermal power plant,the running state of coal mill directly affects the running performance of thermal power unit.Therefore,fault diagnosis of coal mill is of practical significance to ensure the safety of power plant production.Aiming at problems such as uncertain failure type and time lag in fault diagnosis during the actual operation of the coal mill,this paper proposed a fault diagnosis method for the coal mill based on wavelet packet-LSTM neural network.First,a prediction model for the outlet pressure and outlet temperature of the coal mill was established by utilizing the LSTM neural network.The normal data and fault data during the operation of the coal mill were combined into mixed data which be used as the input of the LSTM neural network prediction model to predict the outlet pressure and temperature of the coal mill and obtain the residual signa.Secondly,the wavelet packet decomposition method was used to distinguish and identify the sudden abnormal points of the residual signal.The correlation degree method was used to diagnose the fault types of coal mills by analyzing the trend of changes in all variables of partial data before and after the failure in the coal mill fault library and the mixed data.The results show that the average relative error of the outlet pressure and outlet temperature of the coal mill predicted by the LSTM neural network is not more than 1%.The Wavelet packet decomposition method is used for the residual signal,which can more accurately confirm the time point of the fault.The correlation coefficient method is used to analyze the change trend of all variables,which can identify the type of failure of the coal mill.

关 键 词：磨煤机 LSTM 小波包 相关程度 故障诊断 

分 类 号：TP15[自动化与计算机技术—控制理论与控制工程]