舰船电子工程2023，Vol.43Issue(10)：61-66,6.DOI:10.3969/j.issn.1672-9730.2023.10.014
基于多融合人工智能的退化故障预后方法
Prognostic Method of Degradation Fault Based on Multi-fusion Artificial Intelligence
李泳龙 1王振东 1翟玉婷2
作者信息
- 1. 海军大连舰艇学院学员一大队 大连 116018
- 2. 海军大连舰艇学院信息系统系 大连 116018
- 折叠
摘要
Abstract
There are many problems in the prognosis of current equipment degradation faults,such as few historical monitor-ing data samples,difficult to collect degradation fault samples,static fault prediction and so on.Therefore,the prognostic method of degradation fault based on multi-fusion artificial intelligence is proposed,in which the dynamic update support vector regression can improve the accuracy of static data prediction,principal components analysis can reduce the dimensionality of multi-dimension-al data,and K-means clustering method can detect degradation faults without fault samples.The feasibility of the proposed method is verified by simulation experiments,in which the root mean square error of dynamic update support vector regression is 0.44947,K-means clustering can detect degradation fault samples on the basis of accurate data prediction.The experimental results show that the proposed method can effectively predict and alarm the equipment degradation faults.关键词
支持向量机回归/主成分降维/K均值聚类分析Key words
support vector regression/principal components analysis/K-means clustering分类
电子信息工程引用本文复制引用
李泳龙,王振东,翟玉婷..基于多融合人工智能的退化故障预后方法[J].舰船电子工程,2023,43(10):61-66,6.
