Nonlinear Process Monitoring Based on Global Preserving Unsupervised Kernel Extreme Learning Machine - Hanyuan Zhang; Xiaogang Deng; Yunchu Zhang; Chuanjing Hou; Chengdong Li; Zheng Xin (IEEE Access, 2019)
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.