A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

被引:0
|
作者
Liu, Yingqian [1 ]
Huang, Qian [2 ]
Li, Huairui [1 ]
Li, Yunpeng [1 ]
Li, Sihan [1 ]
Zhu, Rongsheng [1 ]
Fu, Qiang [1 ]
机构
[1] Jiangsu Univ, Res Ctr Fluid Machinery Engn & Technol, Zhenjiang 212013, Peoples R China
[2] China Nucl Power Engn Co Ltd, Beijing 100840, Peoples R China
基金
中国国家自然科学基金;
关键词
essential service water pumps; model preselection algorithm; unsupervised anomaly detection; fault diagnosis; intelligent condition monitoring; FAULT-DIAGNOSIS; SUPPORT;
D O I
10.3390/asi7040061
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.
引用
收藏
页数:16
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