Industrial process fault detection using weighted deep support vector data description

被引：0
|
作者：
Wang X. [1 ]
Wang Y. [1 ]
Deng X. [1 ]
Zhang Z. [1 ]
机构：
[1] College of Control Science and Engineering, China University of Petroleum, Qingdao
来源：
Deng, Xiaogang (dengxiaogang@upc.edu.cn) | 1600年 / Materials China卷 / 72期
关键词：
Algorithm; Deep learning; Dynamic modeling; Fault detection; Nonlinear processes; Process systems; Support vector data description; Weighting factor;
D O I：
10.11949/0438-1157.20210707
中图分类号：
学科分类号：
摘要：
The traditional support vector data description (SVDD) method essentially uses a shallow learning framework, which makes it difficult to effectively monitor complex faults in nonlinear industrial processes. To solve this problem, a fault detection method based on weighted deep support vector data description (WDSVDD) is proposed. On the one hand, the objective function of SVDD optimization is redefined in the framework of deep learning, and a deep SVDD monitoring model (DSVDD) based on deep features is constructed. The kernel density estimation method is used to calculate the statistical control limit of monitoring indicator. On the other hand, considering the fault sensitivity difference of deep features, a feature weighting layer is added in the DSVDD monitoring model. The weighting factors are computed from the perspectives of the static and dynamic information analysis, respectively, which are used to highlight the influence of fault-sensitive features for improving the fault detection rate. The testing results on one typical chemical process show that the proposed method can monitor the occurrence of complex faults more effectively than the traditional SVDD method. © 2021, Editorial Board of CIESC Journal. All right reserved.
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页码：5707 / 5716
页数：9
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