The paper entitled “Machines’ Behaviour Prediction Tool (BPT) for maintenance applications” written by LMS and CASP has been presented at the 4th Advanced Maintenance Engineering, Services and Technologies (AMEST 2020) conference, which was held virtually on September 10-11, 2020.
Authors: P.Aivaliotis, E.Xanthakis, A.Sardelis
Abstract: One of the most critical metrics for the evaluation of machines’ health status and the maintenance activities management is the Remaining Useful Life (RUL). This paper describes the methodologies and the mechanisms which are developed and evaluated for the RUL prediction in PROGRAMS EU project. More specifically, the extend utilization of physics-based models is the main pillar for the prediction of machines’ components degradation profiles. An advanced methodology for physics-based modelling was designed in PROGRAMS EU project. The digital models were created while a synchronous tuning mechanism was designed and developed to enable the Digital Twin (DT) concept and eventually to ensure the high accuracy prediction of machines’ components future failures. The simulation of the physics-based tuned model provides all the required data for the RUL calculation via the correlation of the expected (nominal) and the predicted (real) behaviour and functionality of machines’ components in the long-term future. All the aforementioned activities are integrated in a common framework called “Machines’ Behaviour Prediction Tool (BPT) for maintenance applications”.