Data-driven Digital Twin approach for process optimization: an industry use case

2018 ◽  
Author(s):  
Nenad Stojanovic ◽  
Dejan Milenovic
Keyword(s):  
Process Optimization ◽  
Data Driven ◽  
Use Case ◽  
Digital Twin

A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines

2021 ◽  
Author(s):  
Paolo Pileggi ◽  
Elena Lazovik ◽  
Ron Snijders ◽  
Lars-Uno Axelsson ◽  
Sietse Drost ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Service Providers ◽  
Black Box ◽  
Careful Consideration ◽  
Knowledge Systems ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Use Case ◽  
Digital Twin

Abstract OEMs, service providers and end-users are moving from preventative to predictive maintenance to minimize the risk of unwanted power plant shut-downs and to maximize profitability. Digital Twin and Machine Learning (ML) are important techniques in this transformation as it complements and improves the traditional expert-based knowledge systems. There is a continued trend to use data-driven, so-called black-box, ML techniques as an improvement over traditional statistical approaches. However, these ML approaches suffer from low interpretability or explainability, making it hard to trust how or why a certain anomaly in the system is detected, limiting the trust in the prediction and making it much less likely to identify the real original cause of the problem. In this paper, we present our lesson learnt from operationalizing ML in a real-world use case that studied data from the 1.85 MWe OPRA OP16 all radial single-shaft gas turbine. We comment on the unforeseen obstacles we uncovered during our ML anomaly detection application and juxtapose them with the high potential value that our novel ML applications and explanation method can provide. ML may be enticing for the predictive maintenance of gas turbines but our lesson makes it clear that operationalizing ML goes beyond merely algorithm specifics. In line with the nature of the Digital Twin, it requires careful consideration of the specialized IT system supporting the algorithm, and the specific process it supports and in which it is deployed.

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.

Towards a Data-Driven Digital Twin for the Aging Prediction of Aircraft Composite Components Airworthiness Accounting for Flight and Environmental Conditions

2021 ◽  
Author(s):  
John Michopoulos ◽  
Charbel Farhat ◽  
Athanasios Iliopoulos ◽  
Nicoleta Apetre ◽  
Steven Rodriguez ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Data Driven ◽  
Digital Twin

Electrode Heterogeneity Understanding through Data-Driven Benchmarking for Manufacturing Process Optimization

2021 ◽  
Vol MA2021-01 (2) ◽  
pp. 192-192
Author(s):  
Marc Duquesnoy ◽  
Elixabete Ayerbe ◽  
Iker Boyano ◽  
Alejandro A. Franco
Keyword(s):  
Process Optimization ◽  
Manufacturing Process ◽  
Data Driven

scholarly journals Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 235
Author(s):  
Paulo Garcia ◽  
Francine Darroch ◽  
Leah West ◽  
Lauren BrooksCleator
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Social Issues ◽  
Social Computing ◽  
Social Systems ◽  
Human Interaction ◽  
Data Driven ◽  
Vulnerable Groups ◽  
Use Case ◽  
Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

scholarly journals Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 431
Author(s):  
Alexios Papacharalampopoulos
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Data Driven ◽  
Driven Systems ◽  
Control Techniques ◽  
Digital Twin ◽  
Order Estimation ◽  
Digital Twins ◽  
Initial Stage ◽  
System Order

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

scholarly journals Data-driven ship digital twin for estimating the speed loss caused by the marine fouling

2019 ◽  
Vol 186 ◽  
pp. 106063 ◽  
Author(s):  
Andrea Coraddu ◽  
Luca Oneto ◽  
Francesco Baldi ◽  
Francesca Cipollini ◽  
Mehmet Atlar ◽  
...  
Keyword(s):  
Data Driven ◽  
Marine Fouling ◽  
Digital Twin

scholarly journals Design of a joint research data platform: A use case for severity assessment

2019 ◽  
Vol 54 (1) ◽  
pp. 33-39
Author(s):  
Steven R Talbot ◽  
Stefan Bruch ◽  
Fabian Kießling ◽  
Michael Marschollek ◽  
Branko Jandric ◽  
...  
Keyword(s):  
Human Factor ◽  
Data Driven ◽  
Use Case ◽  
Severity Assessment ◽  
Joint Research ◽  
Web Based ◽  
Specific Content ◽  
Data Platform ◽  
Basic Understanding ◽  
Minimal Effort

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

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