Quantifying Metrological Redundancy in an Industry 4.0 Environment

AIndustry is moving rapidly towards a fourth industrial revolution in which connectivity and data flow play a crucial role. Sensor networks in so-called Factories of the Future are measuring many different quantities and should be robust against failing sensors and other faults. Therefore, it is good design practice to incorporate some form of redundancy in the network. Part of that redundancy simply consists of having spare parts at hand for when the system hardware fails. In this paper we will rather take a measurement centered approach and assess how much the quality of a measurement - as quantified by its measurement uncertainty - degrades when one or more sensors fail. Various types of metrological redundancy will be defined which may fill a gap in ontology concepts relevant for Industry 4.0. The system performance over time can be evaluated by the proposed metrics which are of a metrological nature as they are 'uncertainty aware'. Finally, the concepts will be applied to datasets produced by two industrial test environments.