Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

scholarly journals Application of DBN and GWO-SVM in analog circuit fault diagnosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiyuan Su ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Experimental Results ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Highpass Filter ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Factor C

AbstractFor large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

Nonlinear Analog Circuit Fault Diagnosis Based on MFDFA Method

2012 ◽  
Vol 263-266 ◽  
pp. 108-113 ◽  
Author(s):  
Jing Yuan Tang ◽  
Jian Ming Chen ◽  
Cai Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Accurate Method ◽  
Svm Classifier ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Detrended Fluctuation ◽  
Nonlinear Analog

This paper presents a fault diagnosis method for nonlinear analog circuit based on multifractal detrended fluctuation analysis (MFDFA) method. The MFDFA method is applied to analysis fault signal and extracts the multifractal features from the raw signal. The selected features are given to SVM classifier for further classification. The data required to develop the classifier are generated by simulating various faults using Pspice software. The simulation results show that the proposed method provides a robust and accurate method for nonlinear circuit fault diagnosis.

scholarly journals A wavelet cluster-based band-pass filtering and envelope demodulation approach with application to fault diagnosis in a dry vacuum pump

2007 ◽  
Vol 221 (11) ◽  
pp. 1279-1286 ◽  
Author(s):  
W Jiang ◽  
S K Spurgeon ◽  
J A Twiddle ◽  
F S Schlindwein ◽  
Y Feng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Experimental Studies ◽  
Vacuum Pump ◽  
Pass Band ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Bearing Defects ◽  
Resonance Band ◽  
Band Pass

A Morlet-like wavelet cluster-based method for band-pass filtering and envelope demodulation is described. Via appropriate choice of wavelet parameters, a wavelet cluster combined with multi-Morlet-like wavelets can be used as a band-pass filter with zero phase shift, flat topped pass-band and rapid attenuation in the transition band. It can be used to extract high frequency natural vibration components. The imaginary part of the Morlet-like wavelet cluster is the Hilbert transformation of its real part. This can be used to implement envelope demodulation and extract the envelope component of the high frequency resonance band. The method is applied for fault diagnosis relating to bearing defects in a dry vacuum pump. It is shown that the fault characteristic frequencies can be extracted effectively. The efficacy of the method is demonstrated in experimental studies.

scholarly journals An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

2020 ◽  
Vol 10 (7) ◽  
pp. 2386
Author(s):  
Sumin Guo ◽  
Bo Wu ◽  
Jingyu Zhou ◽  
Hongyu Li ◽  
Chunjian Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Classification Performance ◽  
Fault Classification ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Learning Machine ◽  
Circle Model

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

scholarly journals Fault Diagnosis of Analog Circuits Based on CFNN

2012 ◽  
Vol 6-7 ◽  
pp. 1045-1050
Author(s):  
Mei Rong Liu ◽  
Yi Gang He ◽  
Xiang Xin Li
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Local Optimum ◽  
Chaotic Mapping ◽  
Movement Characteristic ◽  
Fuzzy Neural ◽  
Global Movement ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

An analog circuits fault diagnosis method based on chaotic fuzzy neural network (CFNN) is presented. The method uses the advantage of the global movement characteristic inherent in chaos to overcome the shortcomings that BPNN is usually trapped to a local optimum and it has a low speed of convergence weights. The chaotic mapping was added into BPNN algorithm, and the initial value of the network was selected. The algorithm can effectively and reliably be used in analog circuit fault diagnosis by comparing the two methods and analyzing the results of the example.

Research on Electronic Equipment Fault Diagnosis Expert System Based on Embedded Linux

2013 ◽  
Vol 683 ◽  
pp. 837-840
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Jia Feng Li ◽  
Xin You Cui ◽  
Yong Wu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Embedded Linux ◽  
Electronic Equipment ◽  
Accuracy Rate ◽  
Embedded Linux System ◽  
Knowledge Updating ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Circuit Fault Diagnosis

This paper elaborate one circuit fault diagnosis method about electronic equipment circuit detection combined with expert system on ARM9 and embedded Linux platform and design CLIPS expert system using DSP combined with CPLD data acquisition, making full use of DSP high for-speed data processing capability and then passing the data to the Embedded Linux system operation. Expert system implemente a real-time fault diagnosis according to the the predefined fault diagnosis Knowledge. Compared with traditional testing equipment, the expert system has the advantage of knowledge updating conveniently, high fault diagnosis accuracy rate, etc.

A Transient Faults Diagnosis Method Based on EEMD, Spectral Kurtosis Theory and Energy Operator Demodulating

2016 ◽  
Vol 693 ◽  
pp. 1524-1531
Author(s):  
Yu Long Wang ◽  
Dong Xiang Chen ◽  
Pan Zhang ◽  
Yong Wang ◽  
Zhi Qiang Yu ◽  
...  
Keyword(s):  
Background Noise ◽  
Energy Operator ◽  
Rotating Machinery ◽  
Band Pass Filter ◽  
Transient Faults ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Band Pass ◽  
Faults Diagnosis ◽  
Shock Pulses

When an early fault turns up in rotating machinery, the normal vibration signal will be modulated with periodic transient shock pulses. It’s significant to diagnose these periodic shock pulses for early fault prognostic and diagnostic tests. Usually demodulating is one of the most effective and common used method. Because of the strong background noise, it’s very difficult to select the parameters of band-pass filter. In this paper, we propose to use Ensemble Empirical Mode Decomposition (EEMD) coordinating with spectrum kurtosis theory to choose the Intrinsic Mode Functions (IMFs) to reduce the background noise and select the parameters of band-pass filter adaptively by fast-kurtogram. Energy operator demodulating method is used to demodulate the rebuilt signal to identify the faults frequencies. Energy operator demodulating displays better accuracy and little edge error. The achieved accuracy in the simulation indicates that this proposed transient faults diagnosis method is highly reliable and applicable in early transient faults diagnosis of industrial rotating machinery.

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