Sensor fault diagnosis in air-condition system based on wavelet neural network

LI Dong-hui; LI Sheng

doi:10.13873/j.1000-97872007.04.030

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Sensor fault diagnosis in air-condition system based on wavelet neural network

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- Sensor fault diagnosis in air-condition system based on wavelet neural network
- Issue 4, Pages: 54-57(2007)
- 作者机构：
  
  1. 天津大学电气与自动化工程学院
  2. 天津大学电气与自动化工程学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872007.04.030
  CLC： TP183;TP277
- Published：2007
- 稿件说明：
移动端阅览
[1]李冬辉,李晟.基于小波神经网络的空调系统传感器故障诊断[J].传感器与微系统,2007(04):54-57.

LI Dong-hui, LI Sheng. Sensor fault diagnosis in air-condition system based on wavelet neural network[J]. 2007, (4): 54-57.
[1]李冬辉,李晟.基于小波神经网络的空调系统传感器故障诊断[J].传感器与微系统,2007(04):54-57. DOI： 10.13873/j.1000-97872007.04.030.

LI Dong-hui, LI Sheng. Sensor fault diagnosis in air-condition system based on wavelet neural network[J]. 2007, (4): 54-57. DOI： 10.13873/j.1000-97872007.04.030.

摘要

详细阐述了小波神经网络（WNN）的原理、结构

并对传统的BP算法进行了改进。以空调系统传感器故障检测问题为目标

提出了基于WNN的故障诊断方法。通过采集天津博物馆中的传感器数据

对训练好的WNN进行了传感器故障诊断能力的验证

对温度传感器的1℃偏差故障、0.05℃/s速率漂移故障、完全故障、与不同方差下的精度等级下降故障进行了仿真

结果表明:这种方法对传感器故障具有很好的诊断效果。

Abstract

The principle and structure of wavelet neural network（WNN） is described.Focusing on air-conditioning system’s sensor fault diagnosis

a new method based on WNN is proposed.Using the actual running data from Tianjin museum building control system

the trained WNN is proved efficient enough to detect the bias of 1 ℃

drift of 0.05 ℃/s

complete faults and various precision degree decrease of sensors in the air-conditioning system.

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references

基于平均空间的故障检测与诊断 [J]. 石志勇,李国章,付建平,黄允华. 信息与控制 . 1999(06)

暖通空调系统传感器的故障检测 [J]. 江亿,朱伟峰. 清华大学学报(自然科学版) . 1999(12)

控制系统的故障检测与诊断技术[M]. 清华大学出版社 , 周东华,孙优贤著, 1994

Detection and diagnosis of AHU sensor faults using principal component analysis method [J] . Shengwei Wang,Fu Xiao. Energy Conversion and Management . 2004 (17)

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