Specialist Machinery Data


Increasing the availability of mobile work machines by forecasting machine status based on global data

  Damage detection by means of an on-board diagnostic system. Copyright: © ghh-fahrzeuge Damage detection by means of an on-board diagnostic system.


Mobile work machines are part of a logistical chain in which the failure of one machine can result in the standstill of other machines. Reliability and availability of the machines are therefore important parameters that have a decisive impact on planning certainty within construction and extraction processes. Mobile work machines are already equipped with a variety of sensors nowadays. Yet cost reasons and a lack of evaluation methods mean that these have so far not been systematically used for condition forecasting.

Research objectives

This project will link together all of the sensor data available on the machine to detect correlations between component damage and signal patterns and to determine the remaining service life of individual machines based on the operating loads actually encountered.

  • Identification of typical damage signal patterns by means of test bed trials
  • Development of a simulation model to determine internal loads based on globally available sensor data
  • Development of a reliability model to determine the remaining service life of individual vehicles
  • Transfer of the results to an on-board diagnostic system and implementation on a real test vehicle

Research and project partners

  • The Institute for Fluid Power Drives and Controls (IFAS)
  • The Institute for Power Electronics and Electrical Drives (ISEA)
  • GHH Fahrzeuge GmbH
  • ELBE Gelenkwellen-Service GmbH
  • Stiebel Getriebebau GmbH & Co. KG
  • Indurad GmbH
  • Bosch Rexroth AG
  • K+S Aktiengesellschaft
  • Kessler & Co. GmbH & Co. KG


Promoted by

This project is funded by the European Regional Development Fund, ERDF.


Project sponsor

Project Management Jülich

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