计算机工程与应用2020，Vol.56Issue(1)：265-271,7.DOI:10.3778/j.issn.1002-8331.1810-0260
基于数据深度的过程工业故障检测方法
Fault Detection Method Based on Data Depth for Process Industry
摘要
Abstract
In order to monitor the quality of production process of the process industry, a fault detection method based on data depth is proposed. The common mahalanobis depth and spatial depth are selected, and using Gaussian kernel func-tion to generalize spatial depth in order to improve the sensitivity of spatial depth to position deviation. This method maps high-dimensional data to one-dimensional depth value by means of depth function(mahalanobis depth and kernelized spa-tial depth), and then constructs asymptotic distribution by combining non-parametric rank statistics to make fault judg-ment. The effectiveness of the proposed method is verified through the Tennessee Eastman(TE)simulation experiment by referring to the two indicators of false alarm rate and detection efficiency and comparing with other methods.关键词
故障检测/数据深度/核空间深度/马氏深度/秩统计量/TE过程Key words
fault detection/data depth/kernelized spatial depth/mahalanobis depth/rank statistic/TE process分类
数理科学引用本文复制引用
车建国,赵赛..基于数据深度的过程工业故障检测方法[J].计算机工程与应用,2020,56(1):265-271,7.基金项目
教育部人文社会科学一般研究项目(No.15YJC630007) （No.15YJC630007）
南开大学亚洲研究中心项目(No.AS1410) （No.AS1410）
南开大学基本科研业务经费项目(No.NKZXB1202) （No.NKZXB1202）
国家自然科学基金(No.71102047). （No.71102047）
