Findings in Support Vector Machines Reported from Shanghai Maritime University ( A Rolling Bearing Fault Diagnosis Technique Based On Recurrence Quantification A nalysis and Bayesian Optimization Svm)

上海海事大学支持向量机研究(基于递归量化分析和贝叶斯优化支持向量机的滚动轴承故障诊断技术)

Robotics & Machine Learning Daily News2024，Issue(Jun.11) ：2-3.

来源：

NETL
NSTL

Findings in Support Vector Machines Reported from Shanghai Maritime University ( A Rolling Bearing Fault Diagnosis Technique Based On Recurrence Quantification A nalysis and Bayesian Optimization Svm)

上海海事大学支持向量机研究(基于递归量化分析和贝叶斯优化支持向量机的滚动轴承故障诊断技术)

扫码查看

摘要

研究人员讨论机器学习的新发现——支持向量机。根据NewsRx记者从上海发来的新闻报道,提出了一种基于经验量化分析(RQA)和贝叶斯优化支持向量机(RQA-Bayes-SVM)的滚动轴承故障诊断技术。构造一个综合描述故障模式和故障程度的特征矩阵。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning - Support Vector Machines. According to news reporting originat ing from Shanghai, People’s Republic of China, by NewsRx correspondents, researc h stated, “A rolling bearing fault diagnosis technique is proposed based on Recu rrence Quantification Analysis (abbreviated as RQA) and Bayesian optimized Suppo rt Vector Machine (abbreviated as RQA-Bayes-SVM). Firstly, analyzing the vibrati on signal with recurrence plot and the nonlinear feature parameters are extracte d with RQA, constructing a feature matrix describing the fault mode and fault de gree comprehensively.”

Key words

Shanghai/People’s Republic of China/Asia/Machine Learning/Support Vector Machines/Shanghai Maritime University

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航