中国机械工程2016，Vol.27Issue(14)：1911-1916,6.DOI:10.3969/j.issn.1004-132X.2016.14.012
基于半监督拉普拉斯特征映射的故障诊断
Fault Diagnosis Based on Semi-supervised Laplacian Eigenmaps
摘要
Abstract
Aiming at solving the problems of insufficient labeled fault samples and high-dimen-sional nonlinear fault data,a fault diagnosis model was proposed based on semi-supervised LE algo-rithm.The model directly extracted the low-dimensional manifold features from the raw high-dimen-sional vibration signals,by implementing LE algorithm.The features were fed into semi-supervised classifier based on LE algorithm.Thereby,the operating conditions of mechanical equipment were recognized.Compared with the traditional methods,the model is able to obviously improve fault rec-ognition performance of rolling bearings and gears.关键词
故障诊断/特征提取/流形学习/半监督拉普拉斯特征映射Key words
fault diagnosis/feature extraction/manifold learning/semi-supervised Laplacian eigen-map(LE)分类
计算机与自动化引用本文复制引用
江丽,郭顺生..基于半监督拉普拉斯特征映射的故障诊断[J].中国机械工程,2016,27(14):1911-1916,6.基金项目
国家自然科学基金资助项目(71171154) （71171154）
湖北省自然科学基金资助项目(2015CFB698) （2015CFB698）
湖北省科技支撑计划资助项目(2014BAA032,2015BAA063) （2014BAA032,2015BAA063）
