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题名: Monitoring and fault diagnosis for industrial process
作者: Li S(李帅); Zhou XF(周晓锋); Shi HB(史海波); Zheng ZY(郑泽宇)
作者部门: 数字工厂研究室
会议名称: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期: June 8-12, 2015
会议地点: Shenyang, China
会议录: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2015
页码: 1356-1361
收录类别: EI ; CPCI(ISTP)
ISSN号: 2379-7711
ISBN号: 978-1-4799-8730-6
关键词: manifold learning ; contribution analysis ; monitoring ; fault diagnosis
摘要: Monitoring and fault diagnosis are crucial for industrial process. In this paper, a simple and efficient manifold learning method is used for process monitoring and fault diagnosis. Firstly, local neighbor relationship of process data is used for process modelling, which divides process data into the embedding space and residual space. Then, different statistics and confidence limits are computed, which can be used for monitoring. Finally, the contribution analysis based on manifold learning is used for fault diagnosis. When the fault variables are found, quality control can be introduced to improve production safety and quality stabilization in industrial process. The manifold learning method is applied for one practical foods industrial production process. The experiment results show the feasibility and efficiency of the manifold learning method for monitoring and fault diagnosis.
语种: 英语
产权排序: 1
WOS记录号: WOS:000380502300248
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/17353
Appears in Collections:数字工厂研究室_会议论文

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Recommended Citation:
Li S,Zhou XF,Shi HB,et al. Monitoring and fault diagnosis for industrial process[C]. 见:2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Shenyang, China. June 8-12, 2015.
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