Robert Boschgmbh

Soundar Srinivasan

Rumi Ghosh

Ruobing Chen

Shan Kang

Prasanth Lade

Zubin Abraham

Subhro Das

Methods, systems, and apparatuses for adapting a predictive model for a manufacturing process. One method includes receiving, with an electronic processor, a plurality of data points for a plurality of manufactured parts and the predictive model. The predictive model outputs a label for a manufactured part provided by the manufacturing process indicating whether the manufactured part is accepted or rejected. The method also includes estimating, with the electronic processor, a drift for each of the plurality of data points and generating, with the electronic processor, an adapted version of the predictive model based on the predictive model and the drift for each of the plurality of data points. In addition, the method includes outputting, with the electronic processor, a label for each of the plurality of manufactured parts using the adapted version of the predictive model.

Model adaptation and online learning for unstable environments

A manufacturing process system comprises any number of assembly stations and test stations, a model unit, and any number of final products is provided. Any of a sample test method and the statistical distribution monitoring method performed by the model unit is configured to monitor the model quality after it is deployed and reduce potential unnecessary costs, such as warranty claim costs as a result of sending bad units to the customers, and rework costs as a result of predicting a good part as bad and wasting additional testing efforts on the bad parts. Further, both methods are configured to maximize the probability of detecting hazardous issues, while having control of the false alarm rate.

System and method for monitoring a manufacturing plant

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Sunnyvale, CA, United States of America

Soundar Srinivasan