A Non-Linear System’s Response Identification using Artificial Neural Networks

Sokouti, Babak; Sokouti, Massoud; Haghipour, Siamak

doi:10.5755/j01.eee.113.7.614

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Up to date, many methods have been proposed for the power station fault diagnosis based on vibration signal, such as wavelet transform [2], higher-order statistics [3], etc. These methods have been combined with artificial neural networks (ANNs) to provide accurate fault detection [4]. However, the problem is that due to the lack of the training samples, the networks easily fall into the local minimum [5].…”

Section: Introductionmentioning

confidence: 99%

Fusing of Multi-Channel Sensors for Power Station Fault Diagnosis in Marine Power Systems

Xiong

Cui

Duan

et al. 2013

ElAEE

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Fault diagnosis of the marine power station is essential to ensure the normal electric supply for the whole ship. In this paper, a new faults diagnosis technique for the power station using the data fusion technique has been proposed. The vibration signals of the power station were recorded by the multi-channel sensors. The independent component analysis (ICA) was adopted as the data fusion approach to find the characteristic vibration signals of the power station faults. Then the wavelet packet was employed to extract the feature vector of the fused vibration signals. In addition, the oil particle features has been extracted using the oil analysis. Lastly, the least square support vector machines (LS-SVM) was used to recognize the fault patterns of the power station. Moreover, the improved particle swarm optimization (PSO) was employed to enhance the learning ability of the LS-SVM. The experimental tests were implemented in a real ship to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have shown that distinguished fault features have been extracted and the fault identification accuracy is acceptable. In addition, the classification rate of the proposed method is superior to the traditional SVM based method.

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