A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management
A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management
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DOI：
10.1016/j.promfg.2021.10.020
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Zhenhua Wu;Jianzhi Li
中科院分区：
文献类型：
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作者：
Zhenhua Wu;Jianzhi Li
数字孪生是工业4.0时代智能制造的重要使能技术。数字孪生模型有效地复制了其物理资产，从而在系统中实现轻松的可视化、智能决策和认知能力。提出了一种面向复杂工程产品的动态数据驱动数字孪生模型框架。为了说明所提出的框架，一个例子的健康管理的飞机发动机进行了研究。该框架通过从各种传感器和工业物联网（IIoT）中提取信息来模拟数字孪生模型，以监测网络和物理领域中发动机的剩余使用寿命（RUL）。然后，利用从线性退化模型中选择的传感器测量值，提出了一种长短期记忆（LSTM）神经网络来动态更新数字孪生模型，该模型可以估计物理飞机发动机的最新RUL。通过与其他机器学习算法（包括基于相似性的线性回归和前馈神经网络）在RUL建模上的比较，这种基于LSTM的动态数据驱动的数字孪生模型提供了一种有前途的工具，可以准确地复制飞机发动机的健康状态。这种基于数字孪生的RUL技术也可以扩展到制造系统的健康管理和远程操作。
Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems.