基于深度学习的模拟电路故障诊断方法
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摘要
针对模拟电路的故障诊断问题,提出了一种基于深度学习的故障诊断方法。首先测量模拟电路各个故障类别的脉冲响应数据,随后应用深度学习中深度信念网络方法进行特征提取,最后将提取的特征用于建立基于极端学习机的故障诊断模型,从而对模拟电路的各个故障类别进行区分。通过四运放双二阶高通滤波器电路的故障诊断实验对提出的故障诊断方法进行了验证。通过对比实验表明,提出的基于深度信念网络的故障特征提取方法明显优于传统的基于小波分析的故障特征提取方法,有助于提高模拟电路故障诊断正确率。
A fault diagnosis method based on deep learning is proposed for analog circuit fault diagnosis. Analog circuit impulse response signals are measured firstly,and then deep belief network method is used to extract features from the signals. Finally,an extreme learning machine based diagnosis model is constructed based on extracted features to identify different fault classes. Four-op-amp biquad highpass filter circuit fault diagnosis is performed to test the proposed fault diagnosis method. Meanwhile,the comparison result reflects that the proposed DBN based features extraction is superior to the traditional the traditional wavelet analysis based features extraction method,which is helpful in improving the analog circuit fault diagnosis accuracy.
A fault diagnosis method based on deep learning is proposed for analog circuit fault diagnosis. Analog circuit impulse response signals are measured firstly,and then deep belief network method is used to extract features from the signals. Finally,an extreme learning machine based diagnosis model is constructed based on extracted features to identify different fault classes. Four-op-amp biquad highpass filter circuit fault diagnosis is performed to test the proposed fault diagnosis method. Meanwhile,the comparison result reflects that the proposed DBN based features extraction is superior to the traditional the traditional wavelet analysis based features extraction method,which is helpful in improving the analog circuit fault diagnosis accuracy.
引文
[1] 刘岱,张卫,张家科,等.数字超声无损探伤系统中模拟电路的设计实现[J].电子器件,2018,41(1):235-239.
[2] 王洪,牛晓灵.基于l2正则化回声状态网络的模拟电路故障诊断[J].电子器件,2017,40(5):1283-1286.
[3] Spina R,Upadhyaya S.Linear Circuit Fault Diagnosis Using Neuromorphic Analyzers[J].IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing,1997;44(3):188-196.
[4] Aminian M,Aminian F.A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks with Wavelet Transform as a Preprocessor[J].IEEE Transactions on Instrumentation and Measurement,2007,56(5):1546-1554.
[5] Chen P,Yuan L,He Y,et al.An Improved SVM Classifier Based on Double Chains Quantum Genetic Algorithm and Its Application in Analogue Circuit Diagnosis[J].Neurocomputing,2016;211:202-211.
[6] Ma Q,He Y,Zhou F.A New Decision Tree Approach of Support Vector Machine for Analog Circuit Fault Diagnosis[J].Analog Integrated Circuits and Signal Processing,2016;88(3):455-463.
[7] Hinton G E,Osindero S,Teh Y W.A Fast Learning Algorithm for Deep Belief nets[J].Neural Computation,2006,18(7):1527-1554.
[8] Lee H,Grosse R,Ranganath R,et al.Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks[J].Communications of the Acm,2011;54(10):95-103.
[9] Chen Z,Li W.Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network[J].IEEE Transactions on Instrumentation and Measurement,2017;66(7):1693-1702.
[10] Chen Y,Zhao X,Jia X.Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015;8(6):2381-2392.
[11] Wong P K,Yang Z,Vong C M,et al.Real-Time Fault Diagnosis for Gas Turbine Generator Systems Using Extreme Learning Machine[J].Neurocomputing,2014;128:249-257.
[12] Tian Y,Ma J,Lu C,et al.Rolling Bearing Fault Diagnosis under Variable Conditions Using LMD-SVD and Extreme Learning Machine[J].Mechanism and Machine Theory,2015;90:175-186.
[2] 王洪,牛晓灵.基于l2正则化回声状态网络的模拟电路故障诊断[J].电子器件,2017,40(5):1283-1286.
[3] Spina R,Upadhyaya S.Linear Circuit Fault Diagnosis Using Neuromorphic Analyzers[J].IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing,1997;44(3):188-196.
[4] Aminian M,Aminian F.A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks with Wavelet Transform as a Preprocessor[J].IEEE Transactions on Instrumentation and Measurement,2007,56(5):1546-1554.
[5] Chen P,Yuan L,He Y,et al.An Improved SVM Classifier Based on Double Chains Quantum Genetic Algorithm and Its Application in Analogue Circuit Diagnosis[J].Neurocomputing,2016;211:202-211.
[6] Ma Q,He Y,Zhou F.A New Decision Tree Approach of Support Vector Machine for Analog Circuit Fault Diagnosis[J].Analog Integrated Circuits and Signal Processing,2016;88(3):455-463.
[7] Hinton G E,Osindero S,Teh Y W.A Fast Learning Algorithm for Deep Belief nets[J].Neural Computation,2006,18(7):1527-1554.
[8] Lee H,Grosse R,Ranganath R,et al.Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks[J].Communications of the Acm,2011;54(10):95-103.
[9] Chen Z,Li W.Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network[J].IEEE Transactions on Instrumentation and Measurement,2017;66(7):1693-1702.
[10] Chen Y,Zhao X,Jia X.Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015;8(6):2381-2392.
[11] Wong P K,Yang Z,Vong C M,et al.Real-Time Fault Diagnosis for Gas Turbine Generator Systems Using Extreme Learning Machine[J].Neurocomputing,2014;128:249-257.
[12] Tian Y,Ma J,Lu C,et al.Rolling Bearing Fault Diagnosis under Variable Conditions Using LMD-SVD and Extreme Learning Machine[J].Mechanism and Machine Theory,2015;90:175-186.