Using digital twin for maintenance applications in manufacturing: State of the Art and Gap analysis

Aivaliotis, P.; Georgoulias, Konstantinos; Alexopoulos, Kosmas

doi:10.1109/ice.2019.8792613

Cited by 41 publications

(25 citation statements)

References 17 publications

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“…Estimation of grasping point locations on objects [14] System/unit Optical object tracking [3] Process Smart manufacturing [16] Process Augmented reality industrial solutions [17] Process Decision support [24,29,55] System Aircraft predictive maintenance [4,49] System Maintenance [16,27] Process System integration [57] System Smart factory and cyber-physical production systems [6] System Optimisation of plant and machinery [46,47] Component Human-robot collaborative system [52,58] System Micro-manufacturing [26,59] MELESSE ET AL.…”

Section: Componentmentioning

confidence: 99%

“…Implementation of DT still faces challenges including lack of detailed methodology and standards, difficulties in collecting and storing large amounts of data [44,70,71], developing data acquisition system, synchronisation problems, modelling of a complex system, lack of awareness, resistance of companies to adopting the technology [59], and difficulties in constructing, understanding, controlling, and simulating real-time changes in the system. High-fidelity models are required to simulate and test the product or process in a virtual environment by reducing development time and cost [26,72] The issue of high investment cost and data security is still a hindrance to many companies making DT part of their daily life [55]. There are also problems related to the lack of suitable business models and the use of digital services and goods is still new and implementation is difficult for many manufacturers [47].…”

Section: Challenges In the Implementation Of Digital Twinmentioning

confidence: 99%

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Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Melesse

Pasquale

Riemma

2021

IET Collab Intel Manufact

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A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after-sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after-sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state-of-the-art and challenges, and to indicate future research possibilities in the field. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Componentmentioning

confidence: 99%

Section: Challenges In the Implementation Of Digital Twinmentioning

confidence: 99%

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Melesse

Pasquale

Riemma

2021

IET Collab Intel Manufact

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“…After several years of in-depth research in the field of virtual modeling, simulation techniques, data analysis and visualization, and since the evolution of information and communications technologies digital twins saw their applications extended to several fields. Some of these fields are chiefly maintenance and product health management PHM [14], realtime monitoring [15], ergonomics [16], education [17], production systems management [18], smart grids [19], energy efficiency and more recently healthcare [20]. This multidisciplinary aspect that the concept acquired as a result of its exploitation for numerous and completely different purposes, and due to its combination with multiple concepts and technologies that included some of digital transformation major actors such as augmented reality, big data and industrial internet of things (IIOT) [21], new requirements and criteria were added for its successful implementation within industrial plants.…”

Section: Concept Developmentmentioning

confidence: 99%

Digital twins: development and implementation challenges within Moroccan context

et al. 2020

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Industrial world today is experiencing its fourth industrial revolution. Predecessor of the automation industry, Industry-4.0 adds a layer of autonomy, intelligence and advanced connectivity to complex industrial systems. This layer creates an interactive and dynamic bridge between virtual systems and physical systems with their upward constraints and requirements in a constantly changing physical environment. Digital twins fall into the category of advanced concepts and technologies that enhance this connectivity. Several projects have been launched in Morocco by the government, industrialists and research communities, initiating the digital transformation of Moroccan industrial manufactories and companies. This paper comes within this framework, firstly to highlight the great potential that digital twins can offer for Moroccan industrial context enhancement, secondly to identify the challenges that can hinder the integration and development of digital twins in Moroccan industrial environment.

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“…The DT concept is widely used in the area of industrial systems maintenance as it saves costs by performing the maintenance only when a warning is raised. A recent state of the art and gap analysis is performed by [1] where various applications of the DT concept and its implementation for the maintenance in industrial manufacturing are presented. The authors give the two main reasons why the DT is used which are design validation and product mastering.…”

Section: Introductionmentioning

confidence: 99%

“…Our DT model is based on product mastering where the product is the electrical generator. The paper [17] referenced by [1] is related to the DT concept in the aeronautic field. The DT built by the authors concerns the design and the maintenance of the U.S. Air Force aircraft.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection for aircraft electrical generator using machine learning in a functional data framework

Boulfani

Gendre

Ruiz‐Gazen

et al. 2020

2020 Global Congress on Electrical Engineering (GC-ElecEng)

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To reduce the number of aircrafts on ground, the electrical design engineers are interested in predicting the oil temperature of the generator during a flight. Changes on the temperature value may indicate an incorrect functioning of the generator. An abnormal behavior can be identified by using machine learning algorithms trained on flights free from any anomalies to predict the generator oil temperature. These predictions can then be compared to the observed values, here the sensor data collected from the aircraft during flight. If the observed value is far from the predicted value, a failure warning is raised and a maintenance action shall be performed. In this paper, we build a digital twin of the electrical generator which predicts the oil generator temperature at a given time thanks to the history of features. We compare several machine learning algorithms and the most promising appears to be a neural network which was implemented as part of the electrical generator digital twin. The digital twin is tested by using real flight data containing generator failures and it is verified that the algorithm is able to detect an anomaly prior to the failure events (early failure detection).

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Using digital twin for maintenance applications in manufacturing: State of the Art and Gap analysis

Cited by 41 publications

References 17 publications

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Digital Twin models in industrial operations: State‐of‐the‐art and future research directions

Digital twins: development and implementation challenges within Moroccan context

Anomaly detection for aircraft electrical generator using machine learning in a functional data framework

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