空调系统传感器故障诊断方法

›› 2011, Vol. 24 ›› Issue (6): 101-.

空调系统传感器故障诊断方法

邓勇,王彦,王超

(1.南华大学电气工程学院,湖南衡阳 421001;2.湖南大学电气与信息工程学院,湖南长沙 410082)

出版日期:2011-06-15 发布日期:2011-06-14
作者简介:邓勇(1986—),男,硕士研究生。研究方向:故障诊断,小波神经网络。

Research on Sensor Fault Diagnosis of the Air-Conditioning

DENG Yong, WANG Yan, WANG Chao

(1.College of Electrical Engineering,Nanhua University,Hengyang 421001,China;
2.College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

Online:2011-06-15 Published:2011-06-14

摘要/Abstract

摘要：

针对空调系统中常见的传感器故障问题,提出了基于小波神经网络(WNN)故障诊断策略。在分析空调系统中传感器主要故障的基础上,建立了传感器故障诊断系统。通过传感器的真实测量值与预测值的残差比较,验证了基于WNN的故障诊断能力,分析了基于WNN与BP神经网络故障诊断的残差比结果。仿真实验表明,基于WNN的故障诊断系统具有结构简单、收敛速度快、诊断结果准确、精度高的特点。

关键词: 小波神经网络;BP神经网络;传感器;故障诊断;残差比

Abstract:

A wavelet neural network (WNN) fault diagnosis strategy is proposed based on the air-conditioning unit in the common sensor faults.Sensor fault diagnosis system is based on the analysis of the primary failure of air-conditioning unit sensors.By comparing residuals of the sensor's actual measuring data and forecast data,it verifies the ability of fault diagnosis based on WNN.Besides,the residuals ratio in fault diagnosis base on the WNN and BP neural network is analyzed.Simulation shows that the fault diagnosis system based on the WNN has the advantages of simple structure,fast convergence rate,accurate diagnosis,and higher precision.

Key words: wavelet neural network;BP neural network;sensor;fault diagnosis;residual ratio

中图分类号:

TN707

邓勇, 王彦, 王超. 空调系统传感器故障诊断方法[J]. , 2011, 24(6): 101-.

DENG Yong, WANG Yan, WANG Chao. Research on Sensor Fault Diagnosis of the Air-Conditioning[J]. , 2011, 24(6): 101-.

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