Application of Distributed Machine Learning Model in Fault Diagnosis of Air Preheater

被引：0

作者：

Lei, Haokun ^{[1
]}

Liu, Jian ^{[2
]}

Xian, Chun ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Civil & Resource Engn, Beijing, Peoples R China

[2] Univ Sci & Technol Beijing, Key Lab Minist Efficient Min & Safety Met Mines, Beijing, Peoples R China

来源：

2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019) | 2019年

关键词：

distributed machine learning; fault diagnosis; spark; air preheater;

D O I：

10.1109/icsrs48664.2019.8987707

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Existing monitoring systems for the current operational status of power equipment and fault diagnosis detection systems mostly use serial computing methods, and less parallel distributed processing algorithms are used. With the development of intelligent work of power systems, more and more test data of power plant equipment is becoming more and more complex, which puts new demands on the implementation of data processing and the ability of data calculation. In this study, by using spark, two distributed machine learning models for state detection and fault diagnosis are established for the air preheater, and the confusion matrix is used for evaluation. The results show that the random forest model can effectively diagnose the faults of the air preheater.

引用

页码：312 / 317

页数：6

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