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基于数据挖掘的火力发电厂锅炉给水泵振动故障监测

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由于当前锅炉给水泵振动故障监测方法监测精度较低,无法及时对给水泵运行中潜在的故障隐患进行监测,因此在传统振动故障监测方法的基础上,引入数据挖掘技术,提出了一种新的给水泵振动故障监测方法.利用分频段控制技术的原理,提取给水泵振动故障频带特征;根据NSET故障监测模型与相似度理论,设定给水泵振动故障监测阈值;采用数据挖掘技术,设计给水泵振动故障监测流程,实现振动故障监测.通过实验分析可知,采用新的监测方法获得监测结果的准确率均在 95.47%以上,且监测速度得到了显著提升,具有较高的可行性.
Data Mining-based Vibration Fault Monitoring of Boiler Feed Pump in Thermal Plants
Current methods of monitoring vibration faults of boiler feed pumps are inadequate in monitoring accuracy,and unable to promptly identify potential hidden dangers in operating feed pumps.Based on the traditional vibration fault mo-nitoring methods,this paper puts forward a new feed pump vibration fault monitoring method by introducing data mining technology.The method realizes vibration fault monitoring by extracting frequency band characteristics of vibration fault through utilizing the principle of frequency division control technology,determining judging threshold for fault monitoring according to NSET fault monitoring model and similarity theory,and designing logic flow of fault monitoring using the da-ta mining technology.Experimental analysis shows that the proposed method can achieve monitoring results with accuracy higher than 95.47%and significantly improved monitoring speed,and has high feasibility.

data miningthermal power plantboiler feed pumpfault monitoringvibration fault

周欣欣

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山西工商学院计算机信息工程学院,山西 太原 030036

数据挖掘 火力发电厂 锅炉给水泵 故障监测 振动故障

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JG202353

2024

电工技术
重庆西南信息有限公司(原科技部西南信息中心)

电工技术

影响因子:0.177
ISSN:1002-1388
年,卷(期):2024.(1)
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