1. Outlier detection in contamination control
- Author
-
Jeffrey Weintraub and Scott Warrick
- Subjects
Contamination control ,Computer science ,Control limits ,Control (management) ,Outlier ,Process (computing) ,Anomaly detection ,Data mining ,computer.software_genre ,computer - Abstract
A machine-learning model is presented that effectively partitions historical process data into outlier and inlier subpopulations. This is necessary in order to avoid using outlier data to build a model for detecting process instability. Exact control limits are given without recourse to approximations and the error characteristics of the control model are derived. A worked example for contamination control is presented along with the machine learning algorithm used and all the programming statements needed for implementation.
- Published
- 2018
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