16MO2: Control and Prognostic of Elecro-Hydraulic Machines

Project Leader: Andrea Vacca, Professor of Mechanical Engineering

Institution: Purdue University

This project will formulate the control approach for load handling hydraulic machines that combine oscillation damping features with system prognostic functions.

This study proposes a control approach for load handling hydraulic machines that combines oscillation damping features with system diagnostic functions. The proposed approach has been developed considering the case study of a hydraulic crane for truck applications.The occurrence of oscillations leads to a reduction of productivity and safety in hydraulic machines. In the last decades many pure hydraulic solutions have been proposed; these solutions consist in the introduction of additional components (hydraulic compensators, accumulators, orifices, etc.) and usually lead to energy losses and machine slow down. To overcome these drawbacks different electro-hydraulic solutions have been proposed both in open loop and closed loop (pressure feedback, position feedback). In any system the occurrence of deterioration and faults leads to unsafe operation, loss of productivity and machine down time. The development of a Prognostics and Health Management (PHM) algorithm which evaluates the actual state and the future trend of the system can increase the system productivity and decrease the machine down time. Many different approaches for diagnostics and prognostics have been proposed and they can be classified in two main categories: data-driven methods and model based methods. This study presents an active control for oscillation damping along with a diagnostic algorithm focused on the hydraulic pump; this is the first step in the development of a PHM algorithm for the whole system.

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The award period for this CCEFP research project ended June 30, 2018.  For results, impacts, and future opportunities, please contact the project leader.