2026-07-09T08:04:02Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/145402026-06-13T06:15:48Zcom_20.500.11984_473com_20.500.11984_14090col_20.500.11984_478

00925njm 22002777a 4500 dc Olaizola, Jon author Bouganis, Christos Savvas author Sáenz de Argandoña, Eneko author Iturrospe, Aitzol author José Manuel, Abete author 2020 The ability to monitor the quality of the metal forming process as well as the machine's condition is of significant importance in modern industrial processes. In the case where a physical device (i.e., sensor) cannot be deployed due to the characteristics of the system, models that rely on the estimation of both the applied force and the dynamic behavior of the machine (i.e., system) are adopted. The development of such models and the corresponding algorithms used to estimate the above-mentioned quantities has attracted the interest of the community. The main contribution of this paper is the estimation of a servo press force by employing a novel dual particle filter based algorithm, achieving a maximum relative error in the force estimation of 3.6%. Moreover, to address real-time performance requirements, this paper proposes a field programmable gate array based accelerator that improves the sampling rate by a factor of 200 compared to a processor-based solution, thus enabling the deployment of the system in many realistic scenarios. 0278-0046 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=153213 https://hdl.handle.net/20.500.11984/14540 Dual particle filter (dPF) field programmable gate array (FPGA) model-based soft sensor (MBSS) state unknown input ODS 8 Trabajo decente y crecimiento económico ODS 12 Producción y consumo responsables Real-Time Servo Press Force Estimation Based on Dual Particle Filter