Owing to the raised demands on process operation and product quality, the modern industrial process becomes more complicated when accompanied by the large number of process and quality variables produced. Therefore, quality-related fault detection and diagnosis are extremely necessary for complex industrial processes. Data-driven statistical process monitoring plays an important role in this topic for digging out the useful information from these highly correlated process and quality variables, because the quality variables are measured at a much lower frequency and usually have a significant time delay (Ding 2014; Aumi et al. 2013; Peng et al. 2015; Zhang et al. 2016; Yin et al. 2014). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.
Global Plus Local Projection to Latent Structures
Intelligent Control & Learning Systems
Data-Driven Fault Detection and Reasoning for Industrial Monitoring ; Chapter : 9 ; 147-172
2022-01-03
26 pages
Article/Chapter (Book)
Electronic Resource
English
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