Filling the gap: Comparison of proxy measurement techniques for small data scenarios

[摘要] In the last decade, data availability of industrial processes have gained increasing importance due to the rise of new digital technologies. In practise, many data gaps still exist as a result of missing or broken measuring devices that impede the endeavour of comprehensive data availability. Proxy metering devices represent an easy to adopt solution for missing data by approximating unknown parameters with a mathematical model that is set up on process knowledge and available online meters. To date, the dissemination of proxy metering devices in industrial surroundings is limited due to a lack of general guidelines and the high complexity of available cases. Thus, this research provides comprehensive instructions for developing proxy meter devices on the basis of small datasets. Applied complexity measures categorise five case datasets depending on the inherent intricacy. Based on this classification, the performances of three regression algorithms (multiple linear regression, partial least squares regression, and neural networks) are analysed in combination with dataset modifications techniques bootstrap and artificial noise injection. The results highlight in particular the positive influence of replicating a small training dataset by bootstrapping for neural networks and when the addition of artificial noise is appropriate. (C) 2020 The Author(s). Published by Elsevier Ltd.

[发布日期] 2020-12-01

[关键词] Advanced monitoring;Soft sensor;Virtual sensor;Bootstrap;Artificial noise injection