西安电子科技大学学报（自然科学版）2016，Vol.43Issue(5)：70-74,152,6.DOI:10.3969/j.issn.1001-2400.2016.05.013
面向复杂工业大数据的实时特征提取方法
Real time feature extraction method for complex industrial big data
摘要
Abstract
Industrial big data have the traits of big volume , multi‐sources , continuous sampling and low value density , which results in high complexity , real‐time and high abnormality . Traditional feature extraction methods cannot meet the real‐time requirements of complex industrial big data . In addition , the processing method for industrial big data is different from the internet data stream processing method , which has a higher accuracy requirement . Therefore , this paper proposes a robust incremental on‐line feature extraction method as the Robust Incremental Principal Component Analysis . It uses the sliding window to update new coming data dynamically and filter the abnormal data in windows , then the incremental principal component analysis is implemented on data in windows in order to meet the accuracy and real‐time requirements of industrial big data processing . Experimental results show that the proposed method can effectively extract the data stream in real time with high accuracy .关键词
工业大数据/实时性与鲁棒性/滑动窗口/主成分分析/离群点检测/特征提取Key words
industrial big data/real-time and robustness/sliding window/principal component analysis/outlier detection/feature extraction分类
信息技术与安全科学引用本文复制引用
孔宪光,章雄,马洪波,常建涛,牛萌..面向复杂工业大数据的实时特征提取方法[J].西安电子科技大学学报（自然科学版）,2016,43(5):70-74,152,6.基金项目
中央高校基本科研业务费大数据群资助项目（BDY231423）；国家自然科学基金资助项目（51505357）；陕西省国际科技合作与交流计划资助项目 （）
