Nonlinear Process Monitoring Based on Global Preserving Unsupervised Kernel Extreme Learning Machine

IEEE Access

卷:7

Xiaogang Deng¹  Chuanjing Hou²  Hanyuan Zhang²  Zheng Xin²  Yunchu Zhang²  Chengdong Li² 

[1] College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China;

[2] School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China;

关键词: Nonlinear process monitoring;    unsupervised extreme learning machine;    global structure analysis;    kernel trick;    support vector data description;   

DOI  :  10.1109/ACCESS.2019.2932017

来源: DOAJ

【 摘 要 】

Recently, the unsupervised extreme learning machine (UELM) technique as a nonlinear data mining approach has been employed to diagnose nonlinear process faults. However, during the dimensionality reduction of process observation data, UELM only aims at mining the local structure feature information of process data and lacks of the ability of preserving the global structure analysis of process data, which would perform unsatisfactorily for monitoring nonlinear process. Furthermore, the choice of the optimal number of hidden layer nodes in UELM method is still a challenging problem. To handle these two tough problems, a new fault detection approach on the basis of global preserving unsupervised kernel extreme learning machine (GUKELM) technique is proposed to monitor nonlinear process effectively. In GUKELM technique, the data global structure preserving framework is naturally incorporated into standard UELM to capture the local structure information as well as to preserve the global structure feature information of nonlinear process observation data during the dimensionality reduction. Meanwhile, to tackle the strong nonlinearity of process observation data, the kernel trick is utilized to successfully solve the challenging issue of setting the optimal number of hidden layer nodes. Based on extracted process data low dimensional feature information from GUKELM method, support vector data description algorithm is adopted to construct the monitoring statistic to detect process fault. Finally, the feasibility and effectiveness of the proposed process monitoring strategy is illustrated through a numerical nonlinear system and the continuous stirred tank reactor process which is a typical nonlinear process.

【 授权许可】

Unknown