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

Abstract

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.

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

“…Esmaiel et al [14] extracted shipradiated noise features and used a particle swarm optimization algorithm to optimize the SVM, achieving a recognition rate of 96.67%. Sheng et al [15] used the least squares SVM optimized by particle swarm optimization algorithm to identify faults in ship power plants, with an accuracy of 95.6%. Ma et al [16] established a balanced manifold expansion model for marine three-axis gas turbine units and introduced a particle swarm optimization algorithm to modify the parameters.…”