scholarly journals A Workflow for Synthetic Data Generation and Predictive Maintenance for Vibration Data

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 386
Author(s):  
Şahan Yoruç Selçuk ◽  
Perin Ünal ◽  
Özlem Albayrak ◽  
Moez Jomâa
Keyword(s):  
Real Life ◽  
Synthetic Data ◽  
Predictive Maintenance ◽  
Classification Model ◽  
Classification Algorithms ◽  
Digital Twin ◽  
Vibration Data ◽  
Twin Model ◽  
Industrial Sectors ◽  
Digital Twins

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.

scholarly journals Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

2020 ◽  
Author(s):  
Jeremy Geogres-Filteau ◽  
Elisa Cirillo
Keyword(s):  
Medical Research ◽  
Health Informatics ◽  
Synthetic Data ◽  
Class Imbalance ◽  
Well Being ◽  
Health Data ◽  
Generative Models ◽  
Digital Twin ◽  
Industrial Sectors ◽  
The Subject

Abstract After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it.Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging.The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above.Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics.We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

scholarly journals On Digital Twin Condition Monitoring Approach for Drivetrains in Marine Applications

2019 ◽  
Author(s):  
Sigrid S. Johansen ◽  
Amir R. Nejad
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Offshore Wind ◽  
Test Rig ◽  
Digital Twin ◽  
Model Based ◽  
Twin Model ◽  
Digital Twins ◽  
Fidelity Model

Abstract A digital twin is a virtual representation of a system containing all information available on site. This paper presents condition monitoring of drivetrains in marine power transmission systems through digital twin approach. A literature review regarding current operations concerning maintenance approaches in todays practices are covered. State-of-the-art fault detection in drivetrains is discussed, founded in condition monitoring, data-based schemes and model-based approaches, and the digital twin approach is introduced. It is debated that a model-based approach utilizing a digital twin could be recommended for fault detection of drivetrains. By employing a digital twin, fault detection would be extended to relatively highly diagnostic and predictive maintenance programme, and operation and maintenance costs could be reduced. A holistic model system approach is considered, and methodologies of digital twin design are covered. A physical-based model rather than a data based model is considered, however there are no clear answer whereas which type is beneficial. That case is mostly answered by the amount of data available. Designing the model introduces several pitfalls depending on the relevant system, and the advantages, disadvantages and appropriate applications are discussed. For a drivetrain it is found that multi-body simulation is advised for the creation of a digital twin model. A digital twin of a simple drivetrain test rig is made, and different modelling approaches were implemented to investigate levels of accuracy. Reference values were derived empirically by attaching sensors to the drivetrain during operation in the test rig. Modelling with a low fidelity model showed high accuracy, however it would lack several modules required for it to be called a digital twin. The higher fidelity model showed that finding the stiffness parameter proves challenging, due to high stiffness sensitivity as the experimental modelling demonstrates. Two industries that could have significant benefits from implementing digital twins are discussed; the offshore wind industry and shipping. Both have valuable assets, with reliability sensitive systems and high costs of downtime and maintenance. Regarding the shipping industry an industrial case study is done. Area of extra focus is operations of Ro-Ro (roll on-roll off) vessels. The vessels in the case study are managed by Wilhelmsen Ship Management and a discussion of the implementation of digital twins in this sector is comprised in this article.

scholarly journals A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7830
Author(s):  
Paweł Stączek ◽  
Jakub Pizoń ◽  
Wojciech Danilczuk ◽  
Arkadiusz Gola
Keyword(s):  
Real Life ◽  
Autonomous Mobile Robots ◽  
Shop Floor ◽  
Operating Environment ◽  
Digital Twin ◽  
Digital Twins ◽  
Management Processes ◽  
Physical Testing ◽  
Test Scenarios

The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company’s production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs.

scholarly journals Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

2020 ◽  
Author(s):  
Jeremy Geogres-Filteau ◽  
Elisa Cirillo
Keyword(s):  
Medical Imaging ◽  
Medical Research ◽  
Synthetic Data ◽  
Well Being ◽  
Health Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Digital Twin ◽  
Industrial Sectors ◽  
The Subject

Abstract After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

Will This Be the Decade of Full Digital Twins for Well Construction?

2021 ◽  
Vol 73 (03) ◽  
pp. 34-37
Author(s):  
Judy Feder
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Data Analytics ◽  
Oil And Gas ◽  
Real Life ◽  
Physical World ◽  
Sensor Technology ◽  
Digital Twin ◽  
Digital Twins

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

Developing the digital twin of the economic, financial, information and logistics inter-cluster cooperation mechanism

2020 ◽  
Vol 26 (7) ◽  
pp. 1448-1468
Author(s):  
S.N. Yashin ◽  
Yu.V. Trifonov ◽  
E.V. Koshelev
Keyword(s):  
Federal District ◽  
Industrial Clusters ◽  
Linear Regression Method ◽  
The Public ◽  
Digital Twin ◽  
Twin Model ◽  
Cooperation Mechanism ◽  
Digital Twins ◽  
Further Development

Subject. This article deals with the issues related to the use of digital twins in order to manage innovation and industrial clusters and the liaison between them. Objectives. The article aims to develop a digital twin model of inter-cluster cooperation within a Federal district of Russia. The Volga (Privolzhsky) Federal District is considered a case study. Methods. For the study, we used a multiple non-linear regression method and a fast simulated annealing (FSA). Results. The article offers and describes a designed digital twin model of inter-cluster cooperation mechanism. Conclusions and Relevance. When reallocating investment and human resources within one federal district, the interests of the population of innovation and industrial clusters should be taken into account, as only just an increase in fixed investment does not always lead to the growth of the region's population. The use of the digital twin model of inter-cluster cooperation mechanism will help avoid premature unreasonable management decisions of the public-policy level regarding the further development of innovation-industrial clusters.

scholarly journals Coverage-Based Classification Using Association Rule Mining

2020 ◽  
Vol 10 (20) ◽  
pp. 7013
Author(s):  
Jamolbek Mattiev ◽  
Branko Kavsek
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Classification Accuracy ◽  
Real Life ◽  
Classification Model ◽  
Classification Algorithms ◽  
Classical Class ◽  
Average Accuracy ◽  
Relevance Measures ◽  
Class Association Rule

Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier that selects “strong” class association rules based on overall coverage of the learning set. The advantage of the proposed classifier is that it generates significantly smaller rules on bigger datasets compared to traditional classifiers while maintaining the classification accuracy. We also discuss how the overall coverage of such classifiers affects their classification accuracy. Performed experiments measuring classification accuracy, number of classification rules and other relevance measures such as precision, recall and f-measure on 12 real-life datasets from the UCI ML repository (Dua, D.; Graff, C. UCI Machine Learning Repository. Irvine, CA: University of California, 2019) show that our method was comparable to 8 other well-known rule-based classification algorithms. It achieved the second-highest average accuracy (84.9%) and the best result in terms of average number of rules among all classification methods. Although not achieving the best results in terms of classification accuracy, our method proved to be producing compact and understandable classifiers by exhaustively searching the entire example space.

A Case Study for the Development and Use of a Well Control Simulator as a Digital Twin of a Real Scenario

2021 ◽  
Author(s):  
Francesco Curina ◽  
Ali Talat Qushchi ◽  
Ahmad Aldany
Keyword(s):  
Petroleum Industry ◽  
Real Life ◽  
Well Control ◽  
Safe Environment ◽  
Digital Twin ◽  
Digital Twins ◽  
Drilling Parameters ◽  
Real Life Situation ◽  
Real Scenario

Abstract Simulators in the petroleum industry have been used mainly for training purposes even though they present different applications like digital twins. In this regard, a simulator must approximate the well environment to reflect operative actions and reactions. This paper describes a case study where a well control simulator has been developed to be used as a digital twin where operators may try different scenarios in a safe environment before applying them to the physical well. To cover all aspects of the operation, the simulator should simulate surface equipment as well as a downhole environment. Numerical modeling techniques and hydraulic simulators are used to design the well response to operations. Different scenarios were established to cover most of the possible downhole problems and equipment malfunctions including electrical and hydraulic failures. The study compares a pre-determined set of KPIs common to three different types of simulation: well control, procedural and an integration of both. The target of the study is to collect the data resulting from the use of the simulator while it replicates a real-life situation. This virtual model of the rig and the well can be used to calibrate the main drilling parameters like SPM, RPM and WOB. The digital twin is also used to optimize operational procedures and improve performance and efficiency of rig crews as well as reduce their response time to possible problems. The results show an increase in performance when the knowledge of the rig is combined with the downhole feedback experience. This proves that training of the crew by reproducing their own equipment allows for a major jump in readiness and faster response with minimal mistakes. In addition, conducting the operation virtually allows the crew to uncover any possible issues before tackling the physical well. This in turn helps to reduce errors and safeguard both well and equipment integrity. This paper discusses the integration of the use of downhole environment behavior into a complete digital twin which will play an important role for providing a source of data for regular case studies concerning well control, Maintenance, Scheduling and other critical decisions. This new method candidates itself as a major contender for the future of simulation in the drilling business and shows the importance of that for reducing risks and errors.

scholarly journals Digital Technologies in the Smart Production Management System

2021 ◽  
Vol 93 ◽  
pp. 01024
Author(s):  
Evgeniy Starikov ◽  
Marina Evseeva ◽  
Irina Tkachenko
Keyword(s):  
Management System ◽  
Production Management ◽  
Manufacturing Sector ◽  
Digital Technologies ◽  
Digital Twin ◽  
Twin Model ◽  
Digital Twins ◽  
Industrial Internet ◽  
Production Management System ◽  
Twin Models

The article analyzes the possibility of using such digital technologies as the Industrial Internet of Things (IIoT), Big Data and the creation of models of digital twins in the formation of intelligent management systems for "smart" production within the framework of the concept of digital transformation of the manufacturing sector Industry 4.0. The essence and features of these technologies, problematic aspects of their implementation in real production enterprises are considered. The concept of the functional structure of the digital production management system of a "smart" enterprise based on the digital twin model is proposed. The conclusion is made about the integrating role of technologies for the development and application of digital twin models in the construction of intelligent control systems for "smart" production.

scholarly journals Integration von Verlässlichkeitsmodellen der Entwicklung in einen Digitalen Zwilling zur Umsetzung einer vorausschauenden Instandhaltung

2020 ◽  
Author(s):  
Iris Gräßler
Keyword(s):  
Condition Monitoring ◽  
Phase State ◽  
Predictive Maintenance ◽  
Mechatronic Systems ◽  
Digital Twin ◽  
Digital Twins ◽  
Advanced Control ◽  
Dependability Analysis ◽  
Set Up ◽  
Operation Phase

The development of mechatronic systems increasingly focuses on dependability aspects as reliability and functional safety. Model-based development of mechatronic systems enables for extensive dependability analysis at early design phases. During operation phase, state-of-the-art condition monitoring algorithms as well as methods of advanced control theory ensure an effective dependability investigation. The models used for development of mechatronic systems include substantial information about the architecture, behavior and dependability of the investigated system. Based on these models, a digital twin for use in predictive maintenance can be set up and combined with condition monitoring data of the real system. The integration of those models into a digital twin offers extensive potentials and supports its setup. The paper at hand describes and illustrates the integration of these models and shows the potentials that arise from the use of digital twins for predictive maintenance.

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