Investigating the Impact of Human in-the-Loop Digital Twin in an Industrial Maintenance Context

2020 ◽  
Author(s):  
Ali Al-Yacoubb ◽  
Will Eaton ◽  
Melanie Zimmer ◽  
Achim Buerkle ◽  
Dedy Ariansyaha ◽  
...  
Keyword(s):  
Digital Twin ◽  
Human In The Loop ◽  
Industrial Maintenance ◽  
The Impact

Impact of UAS with Low Size, Weight, and Power Sensors on Air Traffic Controllers’ Performance and Acceptability Ratings

2020 ◽  
Vol 64 (1) ◽  
pp. 154-158
Author(s):  
Kim-Phuong L. Vu ◽  
Jonathan VanLuven ◽  
Timothy Diep ◽  
Vernol Battiste ◽  
Summer Brandt ◽  
...  
Keyword(s):  
Air Traffic ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Subjective Ratings ◽  
Detection Range ◽  
Human In The Loop ◽  
Air Traffic Controllers ◽  
Low Altitude ◽  
Sector Performance ◽  
The Impact

A human-in-the-loop simulation was conducted to evaluate the impact of Unmanned Aircraft Systems (UAS) with low size, weight, and power (SWaP) sensors operating in a busy, low-altitude sector. Use of low SWaP sensors allow for UAS to perform detect-and-avoid (DAA) maneuvers against non-transponding traffic in the sector. Depending upon the detection range of the low SWaP sensor, the UAS pilot may or may not have time to coordinate with air traffic controllers (ATCos) prior to performing the DAA maneuver. ATCo’s sector performance and subjective ratings of acceptability were obtained in four conditions that varied in UAS-ATCo coordination (all or none) prior to the DAA maneuver and workload (higher or lower). For performance, ATCos committed more losses of separation in high than low workload conditions. They also had to make more flight plan changes to manage the UAS when the UAS pilot did not coordinate DAA maneuvers compared to when they did coordinate the maneuvers prior to execution. Although the ATCos found the DAA procedures used by the UAS in the study to be acceptable, most preferred the UAS pilot to coordinate their DAA maneuvers with ATCos prior to executing them.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

Assessing the impact of automation in pharmaceutical quality control labs using a digital twin

2022 ◽  
Vol 62 ◽  
pp. 270-285
Author(s):  
Tiago Coito ◽  
Miguel S.E. Martins ◽  
Bernardo Firme ◽  
João Figueiredo ◽  
Susana M. Vieira ◽  
...  
Keyword(s):  
Quality Control ◽  
Digital Twin ◽  
Pharmaceutical Quality ◽  
The Impact

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

scholarly journals Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

2021 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Ali Arishi ◽  
Krishna K Krishnan ◽  
Vatsal Maru
Keyword(s):  
Raw Materials ◽  
Early Stage ◽  
Supply And Demand ◽  
Network Models ◽  
Combine System ◽  
Neural Network Models ◽  
Digital Twin ◽  
Twin Model ◽  
The Impact ◽  
Reaction Patterns

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.

scholarly journals Reflection of documents in the digital twin of the company and calculation of their impact on human behavior in the simulation model

E-Management ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 50-60
Author(s):  
M. V. Samosudov
Keyword(s):  
Mathematical Model ◽  
Social System ◽  
Social Systems ◽  
Control Effect ◽  
Economic Space ◽  
Agent Based ◽  
Digital Twin ◽  
The Social ◽  
The Impact ◽  
System Operating

The formation and formation of the Industry 4.0 concept stimulated the discussion of the use of computer technology in various areas of economic activity and, in particular, in the automation of social systems management. The basis of the concept is the inclusion of a virtual image of the social system in the form of a mathematical model or a digital twin of the enterprise in the production and management system. At the same time, it should be noted that today digital twin are created mainly only for technical objects used in the activities of enterprises. The purpose of the article is to demonstrate the possibility of fixing organizational documents as one of the system-forming factors in the digital twin of an enterprise. This circumstance makes it possible, firstly, to more accurately calculate the managerial effects of managers by taking into account the impact of organizational documents on the activities of employees of the enterprise; secondly, to identify conflicts of documents developed by various departments of the company; thirdly, to calculate the content of documents during their development (design), based on the requirements of the situation or a given control effect. This possibility arises due to the use of a comprehensive mathematical model of the social system operating in an active environment. The model is a simulation agent-based model and allows you to calculate the dynamics of the social system in the socio-economic space, which allows its use in decision support systems by managers of any scale and activities to calculate the expected effect of management decisions – the specifics of a particular social system are taken into account by combining the values of the phase variables describing the state of the enterprise. The novelty of the research paper lies in the fact that it shows: the possibility to calculate the influence of organizational documents on the behavior of participants and, consequently, on the result of the social system, as well as the mechanism for converting messages, which are invariants of socio-economic space into information that affects the behavior of participants of relations.

scholarly journals A Generic Asset Model for Implementing Product Digital Twins in Smart Remanufacturing

2021 ◽  
Author(s):  
Mairi Kerin ◽  
Duc Truong Pham ◽  
Jun Huang ◽  
Jeremy Hadall
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Integrated System ◽  
Embedded Sensors ◽  
Functional Requirements ◽  
Digital Twin ◽  
Digital Twins ◽  
The Impact ◽  
The Internet Of Things

Abstract A digital twin is a “live” virtual replica of a sensorised component, product, process, human, or system. It accurately copies the entity being modelled by capturing information in real time or near real time from the entity through embedded sensors and the Internet-of-Things. Many applications of digital twins in manufacturing industry have been investigated. This article focuses on the development of product digital twins to reduce the impact of quantity, quality, and demand uncertainties in remanufacturing. Starting from issues specific to remanufacturing, the article derives the functional requirements for a product digital twin for remanufacturing and proposes a UML model of a generic asset to be remanufactured. The model has been demonstrated in a case study which highlights the need to translate existing knowledge and data into an integrated system to realise a product digital twin, capable of supporting remanufacturing process planning.

scholarly journals Digital Twin of Fuel Cell Hybrid Electric Vehicle: a detailed modelling approach of the hydrogen powertrain and the auxiliary systems

2022 ◽  
Vol 334 ◽  
pp. 06003
Author(s):  
Lorenzo Bartolucci ◽  
Edoardo Cennamo ◽  
Stefano Cordiner ◽  
Vincenzo Mulone ◽  
Ferdinando Pasqualini ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Electric Vehicles ◽  
Cell Hybrid ◽  
Energy Demand ◽  
Hybrid Electric Vehicle ◽  
Transport Sector ◽  
Digital Twin ◽  
Hybrid Electric ◽  
The Impact ◽  
Fuel Cell Hybrid

The transport sector is today a major source of pollutant and greenhouse gas emissions. Fuel Cell Hybrid Electric Vehicles are a solution to reduce its environmental impact, thanks to the zero pollutant tailpipe emissions and longer driving ranges if compared with full electric vehicles. A Digital Twin of a FCHEV is developed in this study, through the assessment of models of mechanical and thermal systems within the vehicle. The Simulink/Simscape model here presented is able to support both the design choices and the test of control strategies. The results obtained allow characterizing the impact of the auxiliary systems on the driving range, whose relative value ranges from 28% to 40% of the overall energy demand depending on the ambient temperature, and the range is between 430 km and 356 km respectively for mild and cold temperature.

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