Simplification of Raw Data Set During the Fault Detection Process

摘要：

A novel simplification of database technique is proposed to explicitly account for compromise of low cost and high detection performance used to fault identification in the practical industrial processes. Based on the principle of Mahalanobis distance, the samples with the similar characteristics are replaced by the mean of them, so that the number of raw data set is reduced easily. Moreover, the supper ball domains of mean and variance of samples are presented, which not only retain the statistical properties of raw data set but also avoid the reduction of data unlimitedly. Finally, numerical examples and simulations are given to illustrate the effectiveness of the proposed method.

关键词： Fault detection Mahalanobis distance Mean Variance Simplification of raw data set

作者: LI Jinna LI Yuan WU Huiyong ZHANG Qingling

作者单位: Department of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, P. R. Information Engineering School, Shenyang University of Chemical Technology, Shenyang, Liaoning 11014 Department of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, P. R. Institute of Systems Science, Northeastern University, Shenyang, Liaoning 110004, P. R. China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 5280-5284

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）