A FAULT DIAGNOSIS MOOEL BASED ON LM NEURAL NETWORK FOR REGENERATIVE HEAT SYSTEM

LUO Guibing; LI Chongxiang

doi:10.19666/j.rlfd.2004.10.005

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A FAULT DIAGNOSIS MOOEL BASED ON LM NEURAL NETWORK FOR REGENERATIVE HEAT SYSTEM

更新时间：2025-09-28

- A FAULT DIAGNOSIS MOOEL BASED ON LM NEURAL NETWORK FOR REGENERATIVE HEAT SYSTEM
- Thermal Power Generation Issue 10, Pages: 15-18(2004)
- 作者机构：
  
  1. 西安交通大学,陕西,西安,710049
  2. ,陕西,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.2004.10.005
  CLC： TM76
- Published：2004
- 稿件说明：
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LUO Guibing, LI Chongxiang. A FAULT DIAGNOSIS MOOEL BASED ON LM NEURAL NETWORK FOR REGENERATIVE HEAT SYSTEM[J]. Thermal power generation, 2004, (10): 15-18.
DOI：

LUO Guibing, LI Chongxiang. A FAULT DIAGNOSIS MOOEL BASED ON LM NEURAL NETWORK FOR REGENERATIVE HEAT SYSTEM[J]. Thermal power generation, 2004, (10): 15-18. DOI： 10.19666/j.rlfd.2004.10.005.

摘要

对火电厂回热系统的故障进行分析

提出了基于Levenberg Marquardt神经网络算法的回热系统故障诊断模型

其算法是梯度法与高斯牛顿法的结合。仿真结果表明

该模型显著缩短了训练时间

降低了跟踪误差

具有很高的准确性

优于常规BP神经网络算法模型

适合用于在线学习与监测

Abstract

Faults in regenerative heat system of the power plant have been analysed

and a new fault diagnosis model based on levenbergMarquardt (LM) neural network for regenerative heat system being put forward. The LM algorithm is a combination of the gradient decent algorithm with the GaussNewton algorithm. Results of simulation show that the new model has greatly reduced the training time and the tracking error

having very high accuracy

being more superior than traditional BP neural network algorithm model

and suitable for online training and monitoring.

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