基于小波和神经网络的传感器故障诊断

吉林大学学报(工学版) ›› 2004, Vol. ›› Issue (3): 491-495.

基于小波和神经网络的传感器故障诊断

李文军¹, 张洪坤², 程秀生²

1. 吉林大学通信工程学院, 吉林长春 130022;
2. 吉林大学汽车工程学院, 吉林长春 130022

收稿日期:2004-01-05 出版日期:2004-07-01

Sensor fault diagnosis based on wavelet and neural network

LI Wenjun¹, ZHANG Hongkun², CHENG Xiusheng²

1. College of Communications Engineering, Jilin University, Changchun 130022, China;
2. College of Automotive Engineering, Jilin University, Changchun, 130022, China

Received:2004-01-05 Online:2004-07-01

摘要/Abstract

摘要： 提出了一种新的基于小波包变换和BP神经网络的传感器突变故障诊断方法。根据小波变换在时域和频域都具有良好的局部化特性对信号特征进行精确定位,根据传感器输出信号的小波包分析提取能量变化率的特征向量,利用BP神经网络进行传感器故障分类。这种方法无需预先建立传感器模型和测量传感器输入信号,通过对小波包系数的削减,减少了冗余数据,提高了故障检测的实时性。仿真实验结果表明了该方法的有效性。

关键词: 人工智能, 传感器突变故障, 小波包变换, 神经网络, 故障诊断

Abstract: A diagnosis method based on wavelet packet transform and BP neural network was proposed to detect and identify sensor abrupt fault. Since wavelet packet transform can accurately localize sensor signal characteristics both in time and frequency domain, it is very suitable for non-stationary signal analysis. After wavelet packet analysis for sensor output, eigenvector of energy changing rate was extracted, and classification of sensor fault was conducted by using BP neural network. The proposed method does not need construction of sensor model and measurement of sensor input. Hence redundant data can be reduced by omitting some wavelet packet coefficients and the capability of fault detection can be improved. Simulation results proved the effectiveness of this method.

Key words: artifical intellrgence, abrupt fault of sensors, wavelet packet transform, neural network, fault diagnosis

中图分类号:

TP271

李文军, 张洪坤, 程秀生. 基于小波和神经网络的传感器故障诊断[J]. 吉林大学学报(工学版), 2004, (3): 491-495.

LI Wenjun, ZHANG Hongkun, CHENG Xiusheng. Sensor fault diagnosis based on wavelet and neural network[J]. 吉林大学学报(工学版), 2004, (3): 491-495.

参考文献

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