中国机械工程2017，Vol.28Issue(21)：2588-2594,7.DOI:10.3969/j.issn.1004-132X.2017.21.013
基于多特征融合和BP-AdaBoost算法的列车关键零件故障自动识别
Automatic Fault Recognition for Key Parts of Train Based on Multi-feature Fusion and BP-AdaBoost Algorithm
摘要
Abstract
An automatic fault recognition method was proposed for the fault detection of the fastening bolts and dust collectors based on multi-feature fusion and BP-AdaBoost algorithm.Firstly,the local binary pattern (LBP),histogram of oriented gradient (HOG) and Haar-like features of the faulty and non-faulty areas were extracted.Then,the principal component analysis (PCA) was used to define the contribution of different features to the fault recognition accuracy,the three features metioned above were fused,and the dimensionality reduction was conducted to the fusion feature.Then the BP-AdaBoost classifier was trained by the fusion features.Finally,the trained classifier and the recognition algorithm were used to detect the dust collector and fastening bolt faults.The experimental results show that the algorithm may adapt to the recognition of two different faults.High recognition accuracy rate,low false ratios and low omission ratios are obtained,and the algorithm is robust to light unevenness and occlusion.关键词
集尘器/安全链锁紧螺栓/特征融合/BP-AdaBoost算法Key words
dust collector/fastening bolt/feature fusion/BP-AdaBoost algorithm分类
信息技术与安全科学引用本文复制引用
孙国栋,汤汉兵,林凯,张杨,赵大兴..基于多特征融合和BP-AdaBoost算法的列车关键零件故障自动识别[J].中国机械工程,2017,28(21):2588-2594,7.基金项目
国家自然科学基金资助项目(51775177,51205115) （51775177,51205115）
