基于机器学习算法的发电设备故障诊断与预测研究
Research on Fault Diagnosis and Prediction of Power Generation Equipment Based on Machine Learning Algorithm
平海峰 1冀云海1
作者信息
- 1. 大化集团股份有限公司热电厂,辽宁大连 116308
- 折叠
摘要
文章针对发电设备故障诊断和预测问题,提出了一种基于机器学习算法的方法.通过采集发电设备的运行数据,使用特征提取和特征选择技术得到有效特征,然后利用支持向量机(SVM)和随机森林(RF)等多个机器学习模型进行分类和预测.实验结果表明,文章所提出的方法能够有效地识别发电设备的故障类型,并进行精准的故障预测,具有较高的可靠性和实用性.
Abstract
Aiming at the problem of fault diagnosis and prediction of power generation equipment,this paper proposes a method based on machine learning algorithm.By collecting the operation data of power generation equipment,the effective features are obtained by using feature extraction and feature selection techniques,and then the classification and prediction are made by using multiple machine learning models such as support vector machine(SVM)and random forest(RF).The experimental results show that the proposed method can effectively identify the fault type of power generation equipment and accurately predict the fault,which has high reliability and practicability.
关键词
机器学习/发电设备/故障诊断/支持向量机Key words
machine learning/power generation equipment/fault diagnosis/support vector machine引用本文复制引用
出版年
2024
NETL
NSTL
万方数据