Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets

Petri Kannisto, David Hästbacka

Abstract

In modern mobile machines, a lot of measurement data is available to generate information about machine performance. Exploiting it locally in machines would enable optimising their operation and, thus, yield competitive advantage and reduce environmental load due to reduced emissions. However, optimisation requires extensive knowledge about machine performance and characteristics in various conditions. As physical machines may be located geographically far from each other, the management of ever evolving knowledge is challenging. This study introduces a software concept to enable centralised management of data refinement performed locally in the machines of a geographically distributed fleet. It facilitates data utilisation in end user applications that provide useful information for operators in the field. Whatever the further data analysis requirements are, multiple preprocessing tasks are performed: it enables outlier limit configuration, the calculation of derived variables, data set categorisation and context recognition. A functional prototype has been implemented for the refinement of real operational data collected from forestry machines. The results show that the concept has considerable potential to bring added value for enterprises due to improved possibilities in managing data utilisation.

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Paper Citation


in Harvard Style

Kannisto P. and Hästbacka D. (2016). Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016) ISBN 978-989-758-203-5, pages 21-30. DOI: 10.5220/0006045600210030


in Bibtex Style

@conference{kmis16,
author={Petri Kannisto and David Hästbacka},
title={Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)},
year={2016},
pages={21-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006045600210030},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)
TI - Enabling Centralised Management of Local Sensor Data Refinement in Machine Fleets
SN - 978-989-758-203-5
AU - Kannisto P.
AU - Hästbacka D.
PY - 2016
SP - 21
EP - 30
DO - 10.5220/0006045600210030