Research on Fault Pattern Analysis of Marine Diesel Engine Based on Random Forest Algorithm

被引：0
|
作者：
Wang, Xian-xin [1 ]
Han, Bing [1 ]
机构：
[1] Shanghai Ship & Shipping Res Inst, State Key Lab Nav & Safety Technol, Shanghai, Peoples R China
来源：
2017 4TH INTERNATIONAL CONFERENCE ON TRANSPORTATION INFORMATION AND SAFETY (ICTIS) | 2017年
关键词：
Marine Low-Speed Diesel Engine; Fault Diagnosis; Feature Selection; Random Forest; GRRF-A;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A feature selection method, GRRF-A, based on guided regularized random forest is proposed in this paper, aiming at solving marine diesel engine fault problem. GRRF-A improves GRRF algorithm according to the characteristics of marine diesel engine by using mean decrease in accuracy as importance measure, which is proven to be effective. The computation cost of GRRF-A is small and its results are relatively accurate and stable, which makes this method more suitable for marine diesel engine diagnosis than other methods discussed in this paper.
引用
收藏
页码：312 / 318
页数：7
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