摘要
为了在短时间内完成对二次循环设备腐蚀故障的精准诊断,提出海水二次循环冷却设备腐蚀故障在线诊断方法。根据冷却设备循环机组的工作参数,构建腐蚀故障出现前的设备正常运行状态函数。结合腐蚀故障产生因素及环境条件设置约束条件,确定冷却设备腐蚀故障位置影响因子。计算每个故障位置的判定系数,以在线确定故障位置。横向对比冷却设备管道中不同位置节点力学数据,并校验分析故障位置判定系数,以获取冷却设备腐蚀故障位置判定系数指标量。创新性地最大化短期记忆网络的池化层,并依据故障位置判定系数及指标量,求解腐蚀故障实际触发值和整定值,以实现设备腐蚀故障的在线诊断。测试结果表明,所提方法的诊断准确率均在97%以上。该方法具有见效快、用时短、诊断准、效果稳的特点,能够应对当前大部分海水二次循环冷却设备腐蚀故障的在线诊断任务。
To complete the accurate diagnosis of corrosion failures in secondary circulation equipment in a short time,the online diagnosis method of corrosion failures in seawater secondary circulation cooling equipment is proposed.According to the working parameters of the cooling equipment circulating unit,the normal operation state function of the equipment before the appearance of corrosion failure is constructed.By combing the corrosion fault generating factors and environmental conditions,the constraints are set,and the influence factor of the corrosion fault location of the cooling equipment is determined.The determination coefficient of each fault location is calcutated,and the fault location online is determined.By side-by-side comparing the mechanical data of nodes at different locations in the cooling equipment pipeline,and calibrating and analyzing the determination coefficients of the fault location,the index quantity of the determination coefficient of the corrosion fault location of the cooling equipment are obtaived.The pooling layer of short-term memory network is maximized innovatively and based on the fault location determination coefficients and index quantities,the actual trigger value and set value of corrosion faults are solved,so as to realize the online diagnosis of corrosion faults of equipment.Test results show that the diagnostic accuracy of the proposed method is above 97%.The method is characterized by fast effect,short time,accurate diagnosis,and stable effect,and it can cope with most of the current online diagnosis tasks of corrosion faults of seawater secondary circulation cooling equipment.
作者
张文帅
苏大鹏
姚海宝
张国磊
邢兆强
ZHANG Wenshuai;SU Dapeng;YAO Haibao;ZHANG Guoei;XING Zhaoqiang(Tianjin SDIC Jinneng Electric Power Co.,Ltd.,Tianjin 300480,China)
出处
《自动化仪表》
CAS
2024年第6期57-62,共6页
Process Automation Instrumentation
关键词
二次循环冷却设备
循环流量
故障特征
腐蚀故障
短期记忆网络
在线诊断
Secondary circalation cooling equipment
Recirculation flow
Fault characterization
Corrosion failure
Short-term memory network
Online diagnosis