SVM And It's Application To Fault Prediction Of Ship Heading Control System

Ship heading control is a complex and important issue. If the heading control system presents malfunction during navigation, it possibly have serious influence, and even causes casualties. Therefore, it is not enough to have the failure diagnosis only after the malfunction. The development of heading control system fault prediction technology could avoid the nonessential suspension, prevents further exacerbation of malfunction, and make the preparation ahead of time to reduce the overhaul time and maximize the economic efficiency. So the study on fault prediction technology of ship heading control system has vital significance. The fault prediction technology of ship heading control system, using SVM with small samples, is investigated.Based on the analysis of ship heading control system fault mode and the cause of the fault, the fault tree model of the system was established which uses downlink method to carry on the qualitative analysis. And it carries on failure modeling and simulation on ship heading control system.Time-series prediction method based on support vector machine was derived. It was applied on the prediction of two time series, sinc function and Lorenz chaotic mapping, and compared with the results of moving average and exponential smoothing method. The simulation results show that support vector machine regression algorithm can get a better predicting precision. On the basis of analysis system malfunctions and the evolutionary process, fault prediction based on support vector regression algorithm model was established.Considering of fault characteristics optimization, the weighted coefficient of weighted SVM optimization and the parameters of SVM optimization are interrelated rather than isolated when Support Vector Machine was applied to fault prediction, the parallel optimization method was proposed; predatory search strategy of animals was introduced to the basic artificial fish swarm algorithm in order to create an improved artificial fish swarm algorithm, which was used in parallel optimization.The simulation results show the effectiveness and superiority of the parallel optimization method and the improved artificial fish swarm algorithm.In the pursuit of training accuracy and training speed on Support Vector Machine for fault prediction, the two SVM algorithm performance indicators were considered from the perspective of multi-objective optimization, and the Pareto approximate solution set was solved using the direct multiple targets optimized method; immune algorithm was introduced into fish swarm algorithm to form the improved immune fish swarm algorithm. And the Pareto approximation solution set was gained by using the improved immune fish swarm algorithms. The simulation results show the effectiveness and the superiority of the multi-objective optimization method and the improved immune fish swarm algorithm.Consider that regression results of different kernel functions are different in support regression algorithm, different SVM algorithms, using polynomial kernel function, RBF kernel function and Sigmoid kernel function, were constructed to establish three SVM-forecasting models of ship heading control system fault prediction, and SVM combination forecasting model based on wavelet network was established in accordance with combination prediction principle.The simulation results demonstrated the superiority of SVM combination forecasting model. After carrying on statistical analysis of the predicted heading angle deviation, the statistical value was compared with the set thresholds. Thus the ship heading control system fault prediction came true. Expert repository was established according to the fault tree, and the fault prediction was visualized depend on C++ Builder 6.0.