舰船机电设备是舰船运行的重要设备，当机电设备出现故障时直接威胁到舰船航行的安全性。舰船机电设备构成复杂，各个设备之间呈现出非线性关系，在短时间内会生成大量的数据信息，需采用时效性好、准确性高的算法辅助完成故障诊断。基于此，本文以概述粗集理论与支持向量机故障诊断方法为基础，提出基于粗集理论的舰船机电设备故障诊断方法，在诊断中结合运用粗集理论与改进后SVM模型，通过实验表明此机电设备诊断模型的诊断准确率高达96%以上。
Ship electromechanical equipment is an important equipment for ship operation. When the electromechanical equipment fails, it directly threatens the safety of ship navigation. The ship's electromechanical equipment is complex in composition, and there is a nonlinear relationship between each equipment. A large amount of data information will be generated in a short period of time. It is necessary to use an algorithm with good timeliness and high accuracy to assist in the completion of fault diagnosis. Based on this, based on the overview of rough set theory and support vector machine fault diagnosis method, this paper proposes a fault diagnosis method for ship electromechanical equipment based on rough set theory. In the diagnosis, the rough set theory and the improved SVM model are combined. The experiments show that The diagnostic accuracy of this electromechanical equipment diagnostic model is over 96%.
2022,44(16): 114-117 收稿日期：2022-03-09
DOI：10.3404/j.issn.1672-7649.2022.16.023
分类号：U665
作者简介：许鑫（1980 - ），男，硕士，副教授，研究方向为机械结构设计、机构运动仿真及运动学算法
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