Predictive maintenance has been proposed to maximize the overall plant availability of modern manufacturing systems. To this end, research has been conducted mainly on data-driven prognostic techniques for machinery equipment individual components. However, the lack of historical data together with the intricate design of industrial machines, e.g. robots, stimulate the use of advanced methods exploiting simulation capabilities. This paper aims to address this challenge by introducing a generic framework for the enhancement of advanced physics-based models with degradation curves. The creation of a robot's simulation model and its enrichment with data from the degradation curves of the robot's components is presented. Following, the extraction of information from degradation curves during the simulation of the robot's dynamic behaviour is addressed. The Digital Twin concept is employed to monitor the health status of the robot and ensure the convergence of the simulated to the actual robot behaviour. The output of the simulation can enable to estimate the future behaviour of the robot and make predictions for the quality of the products to be produced, as well as to estimate the robot's Remaining Useful Life. The proposed approach is applied in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.

已经提出了预测性维护，以使现代制造系统的整体工厂可用性最大化。为此，主要针对机械设备单个组件的数据驱动的预测技术进行了研究。但是，缺乏历史数据以及复杂的工业机械（例如机器人）设计，刺激了利用具有仿真功能的先进方法的使用。本文旨在通过引入通用框架来增强具有退化曲线的基于物理学的高级模型，以应对这一挑战。提出了机器人仿真模型的创建以及机器人组件退化曲线数据的充实。接下来，在模拟机器人的过程中从退化曲线中提取信息 解决了动态行为。Digital Twin概念用于监视机器人的健康状况，并确保仿真器与实际机器人行为的融合。模拟的输出可以估计机器人的未来行为，并预测要生产的产品的质量，并可以估计机器人的剩余使用寿命。拟议的方法应用于白色家电行业的案例研究中，在该案例研究中，该机器人在接下来的18个月内是否会出现故障。模拟的输出可以估计机器人的未来行为，并预测要生产的产品的质量，并估计机器人的剩余使用寿命。拟议的方法应用于白色家电行业的案例研究中，在该案例研究中，该机器人在接下来的18个月内是否会出现故障。模拟的输出可以估计机器人的未来行为，并预测要生产的产品的质量，并估计机器人的剩余使用寿命。拟议的方法应用于白色家电行业的案例研究中，在该案例研究中，该机器人在接下来的18个月内是否会出现故障。