Baseline data used to create statistical process control models are often extracted from a long stream of data that contain observations from both successful and unsuccessful productions. Baseline periods must then be identified to ensure the on-line monitoring based on these SPC models is effective. A method is proposed that identifies baseline periods where the quality is good, the quality variable has a stable distribution, and the time intervals are sufficiently long. The method is shown to be robust to the distribution of the quality variable in tests on a real dataset from a melting process and in simulation experiments.
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