Knowledge Commons of Institute of Automation,CAS
Quality-Related Fault Diagnosis Based on k-Nearest Neighbor Rule for Non-Linear Industrial Processes | |
Ren, Zelin1,2; Tang, Yongqiang1; Zhang, Wensheng1,2 | |
发表期刊 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
ISSN | 1550-1477 |
2021-11 | |
卷号 | 17期号:11页码:1-14 |
摘要 | The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares-k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares-k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach. |
关键词 | fault detection fault diagnosis quality-related non-linear industrial process k-nearest neighbor rule |
DOI | 10.1177/15501477211055931 |
关键词[WOS] | COMPONENT ANALYSIS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61976213] ; National Natural Science Foundation of China[61772525] ; National Natural Science Foundation of China[61906191] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Telecommunications |
WOS记录号 | WOS:000726705600001 |
出版者 | SAGE PUBLICATIONS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46573 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Zhang, Wensheng |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Ren, Zelin,Tang, Yongqiang,Zhang, Wensheng. Quality-Related Fault Diagnosis Based on k-Nearest Neighbor Rule for Non-Linear Industrial Processes[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2021,17(11):1-14. |
APA | Ren, Zelin,Tang, Yongqiang,&Zhang, Wensheng.(2021).Quality-Related Fault Diagnosis Based on k-Nearest Neighbor Rule for Non-Linear Industrial Processes.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,17(11),1-14. |
MLA | Ren, Zelin,et al."Quality-Related Fault Diagnosis Based on k-Nearest Neighbor Rule for Non-Linear Industrial Processes".INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 17.11(2021):1-14. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
RZL-Quality-Related (1256KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论