Name
Title | ||
|---|---|---|
Using Polynomial Smoothing And Data Bounding For The Detection Of Adverse Process Changes In A Chemical Process |
Abstract | ||
|---|---|---|
This paper focuses upon the problem of detecting outliers in a time series used to model a production process in the chemical industry. Significant deviations from the underlying time series pattern, i.e. outliers, indicate an adverse process change or out-of-control situation relative to the model. The underlying process is modeled using either least squares moving polynomial fit smoothing based upon the Savitzky-Golay algorithm21 or data bounding. This makes any outliers in the original data more salient when compared to the smoothed graph. Thus outliers can be detected earlier while the process output is still within standard control limits and product specifications. The proposed algorithms improve upon and complement the conventional control chart, particularly with interdependent observations. The process control capabilities of these methods were successfully tested on an autocorrelated data set taken from a chemical production process with known adverse process changes and assigned causes. These algorithms should be of assistance to the chemical engineer or industrial chemist involved in process and quality control. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1021/ci00020a023 | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
DocType | Volume | Issue |
Journal | 34 | 4 |
ISSN | Citations | PageRank |
0095-2338 | 3 | 1.51 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul R. Sebastian | 1 | 9 | 3.48 |
| David E. Booth | 2 | 109 | 21.34 |
| Michael Y. Hu | 3 | 426 | 55.74 |