Date Published: February 9, 2017
Publisher: Public Library of Science
Author(s): Yifei Yang, Minjia Tan, Yuewei Dai, Yudong Zhang.
A ship power equipments’ fault monitoring signal usually provides few samples and the data’s feature is non-linear in practical situation. This paper adopts the method of the least squares support vector machine (LSSVM) to deal with the problem of fault pattern identification in the case of small sample data. Meanwhile, in order to avoid involving a local extremum and poor convergence precision which are induced by optimizing the kernel function parameter and penalty factor of LSSVM, an improved Cuckoo Search (CS) algorithm is proposed for the purpose of parameter optimization. Based on the dynamic adaptive strategy, the newly proposed algorithm improves the recognition probability and the searching step length, which can effectively solve the problems of slow searching speed and low calculation accuracy of the CS algorithm. A benchmark example demonstrates that the CS-LSSVM algorithm can accurately and effectively identify the fault pattern types of ship power equipments.
Ship power equipments often work under terrible working conditions. Thus, the occurrence of faults is unavoidable. It should be mentioned that serious faults can lead to economic losses and threaten the safety of the crew’s life. However, the structure of ship power equipments is complex, and it is difficult to figure out the fault pattern. Then, it is necessary to analyze the fault pattern of ship power equipments and guarantee the reliability and safety of ship power equipments accordingly.
The LSSVM method introduces the idea of square sum of errors to the objective function of the standard SVM, where a training data with n samples is taken into account.
For LSSVM, precise selection of the parameter in RBF kernel function and the penalty factor is the key to recognize the fault pattern. Since the artificial parameter setting may lead to inaccurate recognition, an improved CS algorithm has been proposed in this paper to optimize parameters. The simulation results have illustrated that the improved CS algorithm can converge to the global optimum quickly, and lead to perfect fault pattern recognition performance of ship power equipments. The improved CS-LSSVM can provide a technical support for maintaining ship power equipments timely and accurately.