Improving Virtual Sensor Models by Censored Online Data

Abstract

Digital twins are able to bridge the physical and the virtual world, which is especially useful in industrial environments. One small, but rather essential, kind of digital twin is the so called virtual sensor model. This type of model is used to enhance or replace a physical sensor in industrial settings to reduce costs or enable information retrieval from inaccessible locations within a machine or process. The virtual sensor model is usually trained once, whereat only a predefined amount of information without any adaptation possibilities on the clients side is provided. As manufacturers want to provide the possibility of custom adaptations for their clients, the virtual sensor models require to incorporate adaptive features, which also have to handle incomplete data recordings, e.g. uncensored data. Traditional offline machine learning approaches are often insufficient for such adaptive requirements, therefore the usage of online learning approaches is gaining increased attention, to avoid high computational, storage and temporal costs. This paper covers the continued training of such adaptive virtual sensor models, focusing on the handling and integration of censored online data. Different approaches to tackle the problems of catastrophic forgetting in online learning and correction of censored data are presented as well as the handling of censored data in online learning environments. The experiments sections compares various scenarios with and without censored data using an industrial dataset and demonstrates the positive influence of different online learning approaches.