Wavelet neural network based fault diagnosis in nonlinear analog circuits

Yin, Shirong; Chen, Guangju; Xie, Yongle

doi:10.1016/s1004-4132(06)60089-3

Cited by 13 publications

(2 citation statements)

References 5 publications

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“…Signals at different scales will have different modulus maximum; therefore, the singularity of the signal can be analyzed by extracting the modulus maximum points in the signal. By constructing a smoothing function to smooth the signals of different scales, the first and second order differentials can be extracted to identify the singular points of the signal [16,17].…”

Section: Modulus Maximum Detectionmentioning

confidence: 99%

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Wang

2020

Complexity

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The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network (CNN) and a fault detection algorithm based on discrete wavelet transform (DWT) are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance.

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Section: Modulus Maximum Detectionmentioning

confidence: 99%

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Wang

2020

Complexity

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“…Fault diagnosis of analog circuits is challenging because of nonlinear effects and poor fault models. Since the 1970s, fault diagnosis of analog circuits has become an active research area, and some research methods have been reported [1][2][3][4] , but most of the reported methods are based on back-propagation (BP) network. An important advantage of BP networks based fault diagnostic system is that fault models are embedded in the output voltages and no explicit models are required.…”

Section: Introductionmentioning

confidence: 99%

Study on Fault Diagnosis in Analog Circuit

Kang

2012

AMR

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Hidden Markov model (HMM) for the diagnosis of incipient faults in analog circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Subsequently, output voltages corresponding to the test frequencies are extracted from the response of analog circuits. Finally, the output voltage vectors are used to form the observation sequences, which are sent to training HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method is effective and has better recognition capability than the popularly used back-propagation (BP) network.

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PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Sheikhan

Shabani

2012

Neural Comput & Applic

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Wavelet neural network based fault diagnosis in nonlinear analog circuits

Cited by 13 publications

References 5 publications

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Study on Fault Diagnosis in Analog Circuit

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

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