The fuel oil supply system of the marine diesel engine contains many components, which fits plenty of sensors to monitor the condition of all components. A fault sample consists of data collected from all the sensors at certain time, which lead the dimension of the fault sample is very high. When the ship is sailing, there is a randomness in fault categories and fault duration, which leads the fault data unbalanced. This paper proposes an appropriate combinational approach to address the above problems. First, to reduce computational complexity, the high dimensional fault samples are converted into the low dimensional ones using the principal component analysis (PCA). Second, a sample size optimization (SSO) strategy is proposed to address the problem of the learning from the imbalanced datasets, which improve the classification performance of support vector machine (SVM). Third, a three-dimensional Arnold mapping is introduced into the particle swarm optimization (PSO) algorithm to improve its generalization capability. Finally, the SVM optimized by the improved PSO is trained as the classifier to identify the ten faults in the fuel oil supply system. Results demonstrate that the average correct diagnosis ratio can be as high as 93.9%.
In order to solve the problem of ship's curve trajectory-tracking control, the Norrbin nonlinear response model which can accurately describe the ship's motion state is selected in this paper. The hyperbolic tangent function is used to design the expected hemispheric angle equation, then the complex track control is transformed into a heading control problem. The fast terminal sliding mode (FTSM) is introduced together with the Backstepping control technique to reduce the system adjustment time, eliminate the chattering. Bying combined with extended states observer (ESO) and dynamic surface control (DSC) technique, the internal and external disturbances in real-time can be estimated and compensated, and the "explosion of complexity" caused by backstepping technique is solved. The state of the control system is bounded and stable, and the system error converges to zero. Matlab simulation proves that the controller can realize the trajectory-tracking control quickly and accurately, and has strong robustness to external disturbances. INDEX TERMSFast terminal sliding mode; Extended states observer; Ship trajectory-tracking control; Robustness I.INTRODUCTION
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