Model adaptation and online learning for unstable environments

A system for adapting a predictive model for a manufacturing process, the system comprising:

• a database storing at least one of a first plurality of data points associated with a first plurality of manufactured parts and a second plurality of data point associated with a second plurality of manufactured parts;
• and
• a controller communicably coupled to the database, the controller including an electronic processor, the electronic processor configured to
• receive the second plurality of data points;
• receive the predictive model, the predictive model outputting a label for a manufactured part provided by the manufacturing process, wherein the label indicates whether the manufactured part is accepted or rejected, the predictive model trained using the first plurality of data points associated with the first plurality of manufactured parts and an actual label for each of the first plurality of manufactured parts;
• determine, with the predictive model, a first predicted label for each of the second plurality of manufactured parts;
• estimate a drift for each of the second plurality of data points, wherein the drift for each of the second plurality of data points is estimated by
• training a temporary predictive model using the second plurality of data points and the first predicted label for each of the second plurality of manufactured parts,
• determining, with the temporary predictive model, a second predicted label for each of the second plurality of manufactured parts, and
• comparing the first predicted label and the second predicted label to estimate the drift for each of the second plurality of data points;
• generate an adapted version of the predictive model based on the predictive model and the drift for each of the second plurality of data points;
• and
• output a third predicted label for each of the second plurality of manufactured parts using the adapted version of the predictive model.

A non-transitory computer-readable medium containing instructions that, when executed by an electronic processor, perform a set of functions comprising:

• receiving a predictive model, the predictive model outputting a label for a manufactured part provided by a manufacturing process, wherein the label indicates whether the manufactured part is accepted or rejected, the predictive model trained using the first plurality of data points associated with the first plurality of manufactured parts and an actual label for each of the first plurality of manufactured parts;
• receiving a second plurality of data points associated with a second plurality of manufactured parts;
• determining, with the predictive model, a first predicted label for each of the second plurality of manufactured parts;
• estimating a drift for each of the second plurality of data points, wherein the drift for each of the second plurality of data points is estimated by
• training a temporary predictive model using the second plurality of data points and the first predicted label for each of the second plurality of manufactured parts,
• determining, with the temporary predictive model, a second predicted label for each of the second plurality of manufactured parts, and
• comparing the first predicted label and the second predicted label to estimate the drift for each of the second plurality of data points;
• generating an adapted version of the predictive model based on the predictive model and the drift for each of the second plurality of data points;
• and
• outputting a third predicted label for each of the second plurality of manufactured parts using the adapted version of the predictive model.