A New Neural Network Approach to Electronic Circuit Fault Diagnosis

Tian, Wenjie; Geng, Yu

doi:10.1109/etcs.2009.318

Cited by 1 publication

(2 citation statements)

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“…Sixteen fault symptoms of condenser are selected as the input vector, which are defined as S(S 1 ,S 2 ,S 3 ,S 4 ,S 5 ,S 6 ,S 7 ,-S 8 ,S 9 ,S 10 ,S 11 ,S 12 ,S 13 ,S 14 ,S 15 ,S 16 ) and shown in Table 1. Fourteen kinds of failure mode are selected as the output vector, which are defined as F(F 1 ,F 2 ,F 3 ,F 4 ,F 5 ,F 6 ,F 7 ,F 8 ,F 9 ,F 10 ,-F 11 ,F 12 ,F 13 ,F 14 ) [7] and shown in Table 2.…”

Section: The Condenser Fault Diagnosis Based On Bp Networkmentioning

confidence: 99%

“…The definition of particle swarm size is 40, c 1 =c 2 =1.45449, and the maximum speed is v max =0.5. The maximum particle position is 1, and the minimum is -1, w ini =0.95, w end =0.40, the largest number of iterations is T max , and the default error is 10 -4 [5,16]. Input vector is selected as the data in Table 1, and output vector is selected as the data in Table 2.…”

Section: Condenser Fault Diagnosis Based On Pso-bp Neural Networkmentioning

confidence: 99%

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A Comparative Research on Condenser Fault Diagnosis Based on Three Different Algorithms

Xia¹,

Shuotao²,

Zhang³

et al. 2014

TOEEJ

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Abstract:In view of artificial neural network, there are some deficiencies in condenser fault diagnosis. The BP neural network used for condenser fault diagnosis is highly nonlinear pattern recognition and high precision of fault diagnosis. The PSO-BP neural network can effectively solve the problem of BP neural network that training time is long and training process is easy to fall into the local minimum. The training results of PSO-BP network in convergence speed and convergence effect are significantly improved. Under the condition of small samples, the calculation results of SVM method are better than the calculation result of the other two methods. Although the recognition ratio of improved PSO-BP(2) and SVM is the same, training time of improved PSO-BP (2) is longer than training time of SVM. The generalization ability of SVM is stronger, and the efficiency of SVM is higher than the neural network. With MATLAB programming, three different algorithms, which are BP neural network, PSO-BP neural network and SVM, are studied and compared for the performance of condenser fault diagnosis. In the models of this study, the research results show that condenser fault diagnosis based on SVM has the fastest convergence speed and the best accuracy.

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Section: The Condenser Fault Diagnosis Based On Bp Networkmentioning

confidence: 99%

Section: Condenser Fault Diagnosis Based On Pso-bp Neural Networkmentioning

confidence: 99%