Monitoring of quality-relevant and quality-irrelevant blocks with characteristic-similar variables based on self-organizing map and kernel approaches. (January 2019)

• Record Type: Journal Article
• Authors: Yan, Shifu; Huang, Junping; Yan, Xuefeng
• Is Part Of: Journal of process control. Volume 73(2019)
• Page Start: 103
• Page End: 112
• Publication Date: 2019-01
• Subjects: Quality-related process monitoring -- Self-organizing map -- Kernel partial least squares -- Kernel principal component analysis -- Block monitoring; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.12.009
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A new quality-related and quality-unrelated blocks monitoring scheme is proposed. A variable space division strategy based on self-organizing map is presented. Fault detections of quality-related and quality-unrelated blocks are conducted by KPLS and KPCA methods, respectively. SOM-KPLS/KPCA has better monitoring performance than traditional methods. Abstract: Variables in quality-related process monitoring can be divided into quality-relevant and quality-irrelevant groups depending on the correlation with the quality indicator. These variables can also be separated into multiple sets in which variables are closely relevant to one another because of the interdependence of the process. Block monitoring with reasonable variable partition and reliable model can distinguish quality-related and quality-unrelated faults and improve monitoring performance. A block monitoring method based on self-organizing map (SOM) and kernel approaches is proposed. After collecting and normalizing the sample data including process variables and quality ones, the data matrix is transposed. The inverted samples are used as the input of SOM, and variables with the same behavioral characteristic and a close correlation are topologically mapped in a similar area. Accordingly, samples can be visually blocked into quality-relevant and independent subspaces. Given the nonlinearity of industrial process, kernel partial least squares (KPLS) and kernel principal component analysis (KPCA) are employed to monitor the two types of blocks. The information provided by fault detection can reveal the effects on quality indicators and the location of faults. Finally, the effectiveness of SOM-KPLS/KPCA is evaluated using a numerical example and the Tennessee–Eastman process.