Pr. Sebastian et al., USING POLYNOMIAL SMOOTHING AND DATA BOUNDING FOR THE DETECTION OF ADVERSE PROCESS CHANGES IN A CHEMICAL PROCESS, Journal of chemical information and computer sciences, 34(4), 1994, pp. 881-889
Citations number
31
Categorie Soggetti
Information Science & Library Science","Computer Application, Chemistry & Engineering","Computer Science Interdisciplinary Applications",Chemistry,"Computer Science Information Systems
This paper focuses upon the problem of detecting outliers in a time se
ries used to model a production process in the chemical industry. Sign
ificant deviations from the underlying time series pattern, i.e. outli
ers, indicate an adverse process change or out-of-control situation re
lative to the model. The underlying process is modeled using either le
ast squares moving polynomial fit smoothing based upon the Savitzky-Go
lay algorithm21 or data bounding. This makes any outliers in the origi
nal data more salient when compared to the smoothed graph. Thus outlie
rs can be detected earlier while the process output is still within st
andard control limits and product specifications. The proposed algorit
hms improve upon and complement the conventional control chart, partic
ularly with interdependent observations. The process control capabilit
ies of these methods were successfully tested on an autocorrelated dat
a set taken from a chemical production process with known adverse proc
ess changes and assigned causes. These algorithms should be of assista
nce to the chemical engineer or industrial chemist involved in process
and quality control.