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Intelligent Machine Operator Identification to Develop Damage-Reducing Operating Strategies for Mobile Machines

Brinkschulte, Lars; Geimer, Marcus

Abstract (englisch):

Mobile machines are exposed to a multitude of influencing factors, such as the working task, the operator and the environmental conditions. This leads to a broad spectrum of load collectives for the machine components. In many cases it is difficult to influence the working task and the environmental conditions under the objective function of achieving the required work goals optimally while at the same time minimizing the component load. The operation of the machine offers a more evident degree of freedom to minimize the component damage. With control systems adapted to the operator, the external environmental conditions and the working task, which instructs the operator to a less damaging operating behaviour or override the damage-initiating control signals, the loads and damage can be reduced. An explicit operator identification is the basis for such control approaches.
This paper presents a method for machine operator identification (MOI) based on Hidden Markov Models (HMM). Through a parameter influence analysis and a combination with operation state recognition (OSR), a machine operator can be successfully identified among others. ... mehr

Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 10.2019
Sprache Englisch
Identifikator ISBN: 978-0-7918-5933-9
KITopen-ID: 1000100827
Erschienen in ASME/BATH 2019 Symposium on Fluid Power and Motion Control
Veranstaltung BATH/ASME Symposium on Fluid Power and Motion Control (2019), Longboat Key, FL, USA, 07.10.2019 – 09.10.2019
Verlag The American Society of Mechanical Engineers (ASME)
Schlagwörter Operator Identification, Machine Learning, Hidden Markov Models, Active Vibration Damping, Intelligent Machines
Nachgewiesen in Dimensions
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