一种基于小波变换与神经网络的传感器故障诊断方法
|
摘要
传感器故障诊断在化工生产中有着重要地位。该文以小波变换与神经网络方法为基础,提出了一种传感器故障诊断的方法。该方法能够有效区分传感器故障造成的信号变化与过程本身正常波动造成的信号变化,同时在训练神经网络时只需要系统正常状态下的样本,克服了传感器故障样本稀少的困难。此外,该方法可以在传感器发生故障后估计出正常的模拟信号。实验证明,该方法能够有效完成故障诊断,并可以判断出传感器的故障类型。
Sensor fault diagnosis plays an important role in chemical engineering. This paper proposes a method for sensor fault diagnosis based on wavelet transform and neural network, which can distinguish signal change caused by sensor fault from normal process dynamics. Furthermore, this method needs only the samples of the system under normal situation while training the neural network and overcomes the difficulties caused by the lack of sensor fault samples. This method can also calculate the normal simulation signal when there is a sensor fault. After a series of experiments, this method is proved to be applicable in fault diagnosis and can identify the fault types.
Sensor fault diagnosis plays an important role in chemical engineering. This paper proposes a method for sensor fault diagnosis based on wavelet transform and neural network, which can distinguish signal change caused by sensor fault from normal process dynamics. Furthermore, this method needs only the samples of the system under normal situation while training the neural network and overcomes the difficulties caused by the lack of sensor fault samples. This method can also calculate the normal simulation signal when there is a sensor fault. After a series of experiments, this method is proved to be applicable in fault diagnosis and can identify the fault types.
引文
[1]Isermann R.Model-based fault-detection and diagnosis–Status and applications[J].Annual Reviews in Control,2005,29(1):71-85.
[2]包健,刘晓蕾.基于信息融合技术的电梯控制系统故障诊断[J].计算机应用研究,2011,28(3):869-876.BAO Jian,LIU Xiaolei.Fault diagnosis of elevator control system based on information fusion[J].Application Research of Computers,2011,28(3):869-876.(in Chinese)
[3]ZHANG Guopeng,WANG Bo.Fault diagnosis of flying control system servo actuator based on Elman neural network[C]//The10th International Conference on Electronic Measurement&Instruments.Chengdu:IEEE,2011.
[4]Rajakarunakaran S,Venkumar P,Devaraj D,et al.Artificial neural network approach for fault detection in rotary system[J].Applied Soft Computing,2008,8(1):740-748.
[5]SU Zhongting,WANG Wei,ZHANG Chunlin,et al.Fault diagnosis of general turret electrically controlled system based on fuzzy neutral network optimized by genetic algorithm[C]//The10th International Conference on Electronic Measurement&Instruments.Chengdu:IEEE,2011.
[6]Palme T,Fast M,Thern M.Gas turbine sensor validation through classification with artificial neural networks[J].Applied Energy,2011,88(11):3898-3904.
[7]ZHANG Jianqiu,YAN Yong.A wavelet-based approach to abrupt fault detection and diagnosis of sensors[J].IEEE Transactions on Instrumentation and Measurement,2001,50(5):1389-1396.
[8]Mallat S,Hwang W L.Singularity detection and processing with wavelets[J].IEEE Transcations on Information Theory,1992,38(2):617-643.
[9]Kozionov A,Kalinkin M,Natekin A,et al.Wavelet-based sensor validation:Differentiating abrupt sensor faults from system dynamics[C]//2011IEEE7th International Symposium on Intelligent Signal Processing.Floriana,Malta:IEEE,2011.
[10]WANG Yazhen.Jump and sharp cusp detection by wavelets[J].Biometrika,1995,82(2):385-397.
[11]Reddy M J,Mohanta D K.A wavelet-fuzzy combined approach for classification and location of transmission line faults[J].Electrical Power and Energy Systems,2007,29(9):669-678.
[12]Nason G P.Wavelet shrinkage using cross-validation[J].Journal of the Royal Statistical Society Series B—Methodological,1996,58(2):463-479.
[13]DAI Shijie,SHI Zhanqun,WANG Jizhong,et al.A comparison of neural networks and model-based methods applied for fault diagnosis of electro-hydraulic control systems[C]//Proceedings of2002International Conference on Machine Learning and Cybernetics.Beijing:IEEE,2002.
[14]郑治真.小波变换及其MATLAB工具的应用[M].北京:地震出版社,2001.
[15]赵金宪,金鸿章.基于小波包和神经网络的瓦斯传感器故障诊断[J].传感器与微系统,2010,29(5):80-82.ZHAO Jinxian,JIN Hongzhang.Gas sensor fault diagnosis based on wavelet packet and neural network[J].Transducer and Microsystem Technologies,2010,29(5):80-82.(in Chinese)
[2]包健,刘晓蕾.基于信息融合技术的电梯控制系统故障诊断[J].计算机应用研究,2011,28(3):869-876.BAO Jian,LIU Xiaolei.Fault diagnosis of elevator control system based on information fusion[J].Application Research of Computers,2011,28(3):869-876.(in Chinese)
[3]ZHANG Guopeng,WANG Bo.Fault diagnosis of flying control system servo actuator based on Elman neural network[C]//The10th International Conference on Electronic Measurement&Instruments.Chengdu:IEEE,2011.
[4]Rajakarunakaran S,Venkumar P,Devaraj D,et al.Artificial neural network approach for fault detection in rotary system[J].Applied Soft Computing,2008,8(1):740-748.
[5]SU Zhongting,WANG Wei,ZHANG Chunlin,et al.Fault diagnosis of general turret electrically controlled system based on fuzzy neutral network optimized by genetic algorithm[C]//The10th International Conference on Electronic Measurement&Instruments.Chengdu:IEEE,2011.
[6]Palme T,Fast M,Thern M.Gas turbine sensor validation through classification with artificial neural networks[J].Applied Energy,2011,88(11):3898-3904.
[7]ZHANG Jianqiu,YAN Yong.A wavelet-based approach to abrupt fault detection and diagnosis of sensors[J].IEEE Transactions on Instrumentation and Measurement,2001,50(5):1389-1396.
[8]Mallat S,Hwang W L.Singularity detection and processing with wavelets[J].IEEE Transcations on Information Theory,1992,38(2):617-643.
[9]Kozionov A,Kalinkin M,Natekin A,et al.Wavelet-based sensor validation:Differentiating abrupt sensor faults from system dynamics[C]//2011IEEE7th International Symposium on Intelligent Signal Processing.Floriana,Malta:IEEE,2011.
[10]WANG Yazhen.Jump and sharp cusp detection by wavelets[J].Biometrika,1995,82(2):385-397.
[11]Reddy M J,Mohanta D K.A wavelet-fuzzy combined approach for classification and location of transmission line faults[J].Electrical Power and Energy Systems,2007,29(9):669-678.
[12]Nason G P.Wavelet shrinkage using cross-validation[J].Journal of the Royal Statistical Society Series B—Methodological,1996,58(2):463-479.
[13]DAI Shijie,SHI Zhanqun,WANG Jizhong,et al.A comparison of neural networks and model-based methods applied for fault diagnosis of electro-hydraulic control systems[C]//Proceedings of2002International Conference on Machine Learning and Cybernetics.Beijing:IEEE,2002.
[14]郑治真.小波变换及其MATLAB工具的应用[M].北京:地震出版社,2001.
[15]赵金宪,金鸿章.基于小波包和神经网络的瓦斯传感器故障诊断[J].传感器与微系统,2010,29(5):80-82.ZHAO Jinxian,JIN Hongzhang.Gas sensor fault diagnosis based on wavelet packet and neural network[J].Transducer and Microsystem Technologies,2010,29(5):80-82.(in Chinese)
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心