Title: A method to perform prognostics in electro-hydraulic machines: the case of an independent metering controlled hydraulic crane
Authors: Yuri Ghini; Andrea Vacca
Addresses: Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA ' Maha Fluid Power Research Center, Purdue University, West Lafayette, IN, USA
Abstract: Remaining useful life (RUL) estimation is a topic that has gained more and more attention in the field of fluid power systems, thanks to the tremendous potentials shown for improving safety and reduced production losses. However, many challenges related to RUL estimation are not solved yet, mainly connected to the definition of the health status of each component. A prognostic method based on a data-driven methodology for hydraulic systems is here proposed to estimate the percentage of life already spent by the monitored components. The potentials of the methodology are shown considering the case of a truck-mounted hydraulic crane, for which a simulation model was available. An artificial neural network is designed along with a fuzzy logic system to attain the estimation goal on supply pump and hydraulic control valve. The simulation results show how this method could be used as reference for developing prognostic methods applicable to hydraulic machines.
Keywords: prognostic; remaining useful life; RUL; artificial neural network; ANN; hydraulic machine.
International Journal of Hydromechatronics, 2018 Vol.1 No.2, pp.197 - 221
Available online: 22 Jun 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article