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Maintenance Cost Minimization for an Agricultural Harvesting Gripper.

Florina Maria ȘerdeanMihai Dan ȘerdeanSilviu-Dan Mândru
Published in: Sensors (Basel, Switzerland) (2023)
A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution.
Keyphrases
  • public health
  • healthcare
  • mental health
  • machine learning
  • climate change
  • deep learning
  • quantum dots