Monitoring based on MIC-PCA and SVDD for industrial process
文献类型：会议论文
| 作者 | Zhou XF(周晓锋) ; Song H(宋宏); Li S(李帅); Wang ZW(王中伟)
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| 出版日期 | 2017 |
| 会议名称 | 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017 |
| 会议日期 | March 25-26, 2017 |
| 会议地点 | Chongqing, China |
| 关键词 | principal component analysis support vector data description maximal information coefficient process monitoring industrial process |
| 页码 | 1210-1214 |
| 通讯作者 | Wang ZW(王中伟) |
| 中文摘要 | Complex industrial processes are often non-linear and non-Gaussian, while the traditional principal component analysis (PCA) method assumes that the data are Gaussian and linear. In this paper, a novel process monitoring method based on maximum information coefficient-PCA (MIC-PCA) and support vector data description (SVDD) is proposed. First, the covariance matrix is replaced by the MIC matrix which can measure the non-linear correlation between the variables. Then the SVDD models are built in the principal component subspace (PCS) and the residual subspace (RS) to improve the monitoring of non-linear and non-Gaussian processes. Finally, the feasibility and effectiveness of the proposed method are validated by high-pressure and low-density polyethylene (LDPE) industrial process. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | Chongqing Geeks Education Technology Co., Ltd; Chongqing Global Union Academy of Science and Technology; Global Union Academy of Science and Technology; IEEE Beijing Section |
| 会议录 | Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4673-8977-8 |
| WOS记录号 | WOS:000427137300243 |
| 源URL | [http://ir.sia.cn/handle/173321/21245]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Zhou XF,Song H,Li S,et al. Monitoring based on MIC-PCA and SVDD for industrial process[C]. 见:2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2017. Chongqing, China. March 25-26, 2017. |
入库方式： OAI收割
来源：沈阳自动化研究所
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