An official website of the United States government
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.
more »
« less
Multi-sensor Failure Recovery in Aero-Engines Using a Digital Twin Platform: A Case Study
Digital twin technology has found wide applications in the aerospace industry, for fleet monitoring, diagnostics, predictive maintenance, and manufacturing. Sensing the aero-engine in a test environment is challenging, due to issues with noisy and failed sensors, introduced by extreme conditions. Downstream diagnostics and prognostics require that key sensed values are available for the duration of the test for both real-time and off-line analysis. Virtual sensors that mimic the behavior of the physical sensors provide a resilient solution in the presence of such failures. This paper describes virtual sensors designed using a low-cost digital twin platform with off-the-shelf analytical software components and libraries. The platform is a no-code environment, that permits users to rapidly experiment with different analytical models and configurations. We show that virtual sensors can emulate the behavior of the physical sensor(s) in the event of multiple physical sensor failures with high accuracy, the design of which is facilitated by the Digital Twin platform. From a process standpoint, the Digital Twin results in several advantages to the organization including the breaking down of departmental data silos, reevaluation of key assumptions regarding system design, and the standardization of monitoring process. The digital twin approach is seen to be a catalyst for reengineering the design and monitoring lifecycle for industrial organizations.
more »
« less
- Award ID(s):
- 2306109
- PAR ID:
- 10501418
- Editor(s):
- Arai, K.
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Intelligent Computing (SAI 2023)
- ISBN:
- 978-3-031-37717-4
- Format(s):
- Medium: X
- Location:
- London, UK
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Digital twin technology is a virtual depiction of a physical product and has been utilized in many fields. Digital twin patient model in healthcare is a virtual patient that provides opportunities to test the outcomes of various interventions virtually without subjecting an actual patient to possible harm. This can serve as a decision aid in the complex environment of the intensive care unit (ICU). Our objective is to develop consensus among a multidisciplinary expert panel on statements regarding respiratory pathophysiology contributing to respiratory failure in the medical ICU. We convened a panel of 34 international critical care experts. Our group modeled elements of respiratory failure pathophysiology using directed acyclic graphs (DAGs) and derived expert statements describing associated ICU clinical practices. The experts participated in three rounds of modified Delphi to gauge agreement on 78 final questions (13 statements with 6 substatements for each) using a Likert scale. A modified Delphi process achieved agreement for 62 of the final expert rule statements. Statements with the highest degree of agreement included the physiology, and management of airway obstruction decreasing alveolar ventilation and ventilation-perfusion matching. The lowest agreement statements involved the relationship between shock and hypoxemic respiratory failure due to heightened oxygen consumption and dead space. Our study proves the utility of a modified Delphi method to generate consensus to create expert rule statements for further development of a digital twin-patient model with acute respiratory failure. A substantial majority of expert rule statements used in the digital twin design align with expert knowledge of respiratory failure in critically ill patients.more » « less
-
In this work, we integrate digital twin technology with RFID localization to achieve real-time monitoring of physical items in a large-scale complex environment, such as warehouses and retail stores. To map the item-level realities into a digital environment, we proposed a sensor fusion technique that merges a 3D map created by RGB-D and tracking cameras with real-time RFID tag location estimation derived from our novel Bayesian filter approach. Unlike mainstream localization methods, which rely on phase or RSSI measurements, our proposed method leverages a fixed RF transmission power model. This approach extends localization capabilities to all existing RFID devices, offering a significant advancement over conventional techniques. As a result, the proposed method transforms any RFID device into a digital twin scanner with the support of RGB-D cameras. To evaluate the performance of the proposed method, we prototype the system with commercial off-the-shelf (COTS) equipment in two representative retail scenarios. The overall performance of the system is demonstrated in a mock retail apparel store covering an area of 207 m2, while the quantitative experimental results are examined in a small-scale testbed to showcase the accuracy of item-level tag localization.more » « less
-
Nowadays, the data collected in physical/engineering systems allows various machine learning methods to conduct system monitoring and control, when the physical knowledge on the system edge is limited and challenging to recover completely. Solving such problems typically requires identifying forward system mapping rules, from system states to the output measurements. However, the forward system identification based on digital twin can hardly provide complete monitoring functions, such as state estimation, e.g., to infer the states from measurements. While one can directly learn the inverse mapping rule, it is more desirable to re-utilize the forward digital twin since it is relatively easy to embed physical law there to regularize the inverse process and avoid overfitting. For this purpose, this paper proposes an invertible learning structure based on designing parallel paths in structural neural networks with basis functionals and embedding virtual storage variables for information preservation. For such a two-way digital twin modeling, there is an additional challenge of multiple solutions for system inverse, which contradict the reality of one feasible solution for the current system. To avoid ambiguous inverse, the proposed model maximizes the physical likelihood to contract the original solution space, leading to the unique system operation status of interest. We validate the proposed method on various physical system monitoring tasks and scenarios, such as inverse kinematics problems, power system state estimation, etc. Furthermore, by building a perfect match of a forward-inverse pair, the proposed method obtains accurate and computation-efficient inverse predictions, given observations. Finally, the forward physical interpretation and small prediction errors guarantee the explainability of the invertible structure, compared to standard learning methods.more » « less
-
Abstract With rapid urbanization necessitating innovative strategies for urban adaptation, combining technological advancements and holistic methodologies, this research explored the synergy between urban metabolism and digital twin technologies to foster sustainable urban development. A pilot model representing a university building, including the surrounding streetscape, was constructed using the Unreal Engine. By using available CAD design drawings and GIS technologies, the physical spaces were modelled. The physical and analytical environments were integrated into the digital twin; material flow analysis was also conducted. The developed framework aims to offer a detailed visualization of building behaviour, facilitating comparisons with urban metabolism analysis. This approach holds promise for sustainable urban design by integrating diverse data streams through digital twin technologies. The potential impact of this research extends to the tracking, mapping, and analysis of crucial resource flows, such as materials, water, energy, and waste, fostering circular economy strategies within the built environment. Understanding urban metabolism facilitates the identification of resource-efficient opportunities, promoting resource recovery and reuse to reduce the environmental impact of urban cores. Embracing digital twin technologies and urban metabolism analysis offers cities streamlined data collection processes, supporting standardization and sustainable urban practices. This study marks a critical step towards integrating diverse data streams into urban metabolism analysis, aligning with circularity objectives in the built environment. By adopting this framework, cities can better understand new production and consumption patterns that prioritize the responsible use of natural resources, contributing to a more sustainable and resilient future.more » « less
