Machines' Behaviour Prediction Tool (BPT) for maintenance applications

被引:4
作者
Aivaliotis, P. [1 ]
Xanthakis, E. [2 ]
Sardelis, A. [2 ]
机构
[1] Univ Patras, Lab Mfg Syst & Automat, Rion 26504, Greece
[2] CASP SA, 64 Michalakopoulou Str, Athens 11528, Greece
基金
欧盟地平线“2020”;
关键词
Predictive Maintenance; Maintenance Models and Engineering; Simulation and Optimisation in Maintenance;
D O I
10.1016/j.ifacol.2020.11.052
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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". Copyright (C) 2020 The Authors.
引用
收藏
页码:325 / 329
页数:5
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