A Framework for Factory-Trained Virtual Sensor Models Based on Censored Production Data

In Industrial Manufacturing, the process variable is often not directly observable using a physical measuring device. For this reason, Virtual Sensors are used, which are surrogate models for a physical sensor trained on previously collected process data. In order to continuously improve such virtual sensor models, it is desirable to make use of data gathered during production, e.g. to adapt the model to client-specific tools not included in the basic training data. In many real-world scenarios, it is only possible to gather data from the production process to a limited extent, as feedback is not always available. One example of such a situation is the production of a workpiece within required quality bounds. In case the finished workpiece meets the quality criteria or else is irreversibly damaged in the process, there is no feedback regarding the model error. Only in the case where the operator is able to specify a correction to reach the desired target value in a second run, the correction error can be used as an approximation for the model error.