基于深度学习的火电厂发电装备智能故障诊断与预测研究
Research on Intelligent Fault Diagnosis and Prediction of Thermal Power Plant Power Generation Equipment Based on Deep Learning
曾阳 1张莉 2李国朋1
作者信息
- 1. 兖煤菏泽能化有限公司赵楼综合利用电厂,山东菏泽 274700
- 2. 山东华聚能源股份有限公司,山东济宁 273500
- 折叠
摘要
为实现火电厂发电设备的智能故障诊断与预测,提出了一种基于LSTM和CNN相结合的深度学习方法.该模型通过LSTM模块建模时间序列,并使用CNN模块提取空间局部特征,实现了故障模式的识别.结果表明,所设计的模型可以提高故障诊断的平均精度,并且提供了充足的预警时间,大大提高了火电厂发电装备的运行效率和安全性.
Abstract
A deep learning method based on the combination of LSTM and CNN is proposed to achieve intelligent fault diagnosis and prediction of power generation equipment in thermal power plants.This model models time series using LSTM modules and extracts spatial local features using CNN modules,achieving fault pattern recognition.The results show that the designed model can improve the average accuracy of fault diagnosis and provide sufficient warning time,greatly improving the operational efficiency and safety of power generation equipment in thermal power plants.
关键词
深度学习/故障诊断/发电设备Key words
deep learning/fault diagnosis/power generation equipment引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据