Authors:
Petri Kannisto
and
David Hästbacka
Affiliation:
Tampere University of Technology, Finland
Keyword(s):
Distributed Knowledge Management, Mobile Machinery.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Communication, Collaboration and Information Sharing
;
Intelligent Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Symbolic Systems
;
Tools and Technology for Knowledge Management
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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