Uncertainty Evaluation for Metrologically Redundant Industrial Sensor Networks

Sensor networks are ubiquitous in Industry 4.0 environments. Large volumes of data are being produced and need to be processed. If some form of metrological redundancy is present in the network, the question arises how this redundancy can be used to identify and reject possibly faulty sensors and/ or to reduce the measurement uncertainty of the estimate of the measurand. This paper will address these questions. An algorithm, called the LCSS algorithm, will be presented that can be used to identify sensor values that are metrologically of questionable quality and that can identify a largest subset of consistent sensor values. The algorithm will be illustrated on a real dataset produced by an industrial test environment at ZeMA.