Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

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Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

10.20944/preprints202009.0460.v1 ◽

2020 ◽

Author(s):

Sergei Belov ◽

Sergei Nikolaev ◽

Ighor Uzhinsky

Keyword(s):

Data Driven ◽

Machine Learning Techniques ◽

Engineering Systems ◽

Learning Techniques ◽

Prescriptive Analytics ◽

Hybrid Data ◽

Complex Engineering ◽

Data Driven Modeling ◽

Complex Engineering Systems

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

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Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region

IEEE Access ◽

10.1109/access.2019.2920916 ◽

2019 ◽

Vol 7 ◽

pp. 74471-74481 ◽

Cited By ~ 21

Author(s):

Zaher Mundher Yaseen ◽

Wan Hanna Melini Wan Mohtar ◽

Ameen Mohammed Salih Ameen ◽

Isa Ebtehaj ◽

Siti Fatin Mohd Razali ◽

...

Keyword(s):

Data Driven ◽

Tropical Region ◽

Model Case ◽

Hybrid Data ◽

Streamflow Simulation

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Digital Storytelling in Cultural Heritage: Audience Engagement in the Interactive Documentary New Life

Sustainability ◽

10.3390/su13031193 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1193

Author(s):

Anna Podara ◽

Dimitrios Giomelakis ◽

Constantinos Nicolaou ◽

Maria Matsiola ◽

Rigas Kotsakis

Keyword(s):

Cultural Heritage ◽

Full Range ◽

Social Dimension ◽

Data Driven ◽

Specific Interest ◽

Key Factors ◽

Audience Engagement ◽

Main Research ◽

The Social

This paper casts light on cultural heritage storytelling in the context of interactive documentary, a hybrid media genre that employs a full range of multimedia tools to document reality, provide sustainability of the production and successful engagement of the audience. The main research hypotheses are enclosed in the statements: (a) the interactive documentary is considered a valuable tool for the sustainability of cultural heritage and (b) digital approaches to documentary storytelling can provide a sustainable form of viewing during the years. Using the Greek interactive documentary (i-doc) NEW LIFE (2013) as a case study, the users’ engagement is evaluated by analyzing items from a seven-year database of web metrics. Specifically, we explore the adopted ways of the interactive documentary users to engage with the storytelling, the depth to which they were involved along with the most popular sections/traffic sources and finally, the differences between the first launch period and latest years were investigated. We concluded that interactivity affordances of this genre enhance the social dimension of cultural, while the key factors for sustainability are mainly (a) constant promotion with transmedia approach; (b) data-driven evaluation and reform; and (c) a good story that gathers relevant niches, with specific interest to the story.

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Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.126242 ◽

2021 ◽

Vol 296 ◽

pp. 126242

Author(s):

Oliver J. Fisher ◽

Nicholas J. Watson ◽

Laura Porcu ◽

Darren Bacon ◽

Martin Rigley ◽

...

Keyword(s):

Data Driven ◽

Multiple Target ◽

Process Manufacturing ◽

Target Data

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Multivariate modeling of pan evaporation in monthly temporal resolution using a hybrid evolutionary data-driven method (case study: Urmia Lake and Gavkhouni basins)

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09060-8 ◽

2021 ◽

Vol 193 (6) ◽

Author(s):

Alireza Emadi ◽

Sarvin Zamanzad-Ghavidel ◽

Sina Fazeli ◽

Soheila Zarei ◽

Ali Rashid-Niaghi

Keyword(s):

Temporal Resolution ◽

Urmia Lake ◽

Data Driven ◽

Multivariate Modeling ◽

Pan Evaporation

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Data-Driven Modeling and Optimization of Building Energy Consumption: a Case Study

2020 IEEE Power & Energy Society General Meeting (PESGM) ◽

10.1109/pesgm41954.2020.9281663 ◽

2020 ◽

Author(s):

Divas Grover ◽

Yaser P. Fallah ◽

Qun Zhou ◽

P.E. Ian LaHiff

Keyword(s):

Energy Consumption ◽

Building Energy ◽

Data Driven ◽

Building Energy Consumption ◽

Modeling And Optimization ◽

Data Driven Modeling

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Organisational Constraints in Data-driven Maintenance: a case study in the automotive industry

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.11.015 ◽

2020 ◽

Vol 53 (3) ◽

pp. 95-100

Author(s):

P Savolainen ◽

J Magnusson ◽

M. Gopalakrishnan ◽

E. Turanoglu Bekar ◽

A. Skoogh

Keyword(s):

Automotive Industry ◽

Data Driven

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Hybrid-Data Approach for Estimating Trip Purposes

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211018474 ◽

2021 ◽

pp. 036119812110184

Author(s):

Xiaoling Luo ◽

Adrian Cottam ◽

Yao-Jan Wu ◽

Yangsheng Jiang

Keyword(s):

Transportation Systems ◽

Data Driven ◽

Trip Purpose ◽

Additional Information ◽

Point Of Interest ◽

Average Accuracy ◽

Hybrid Data ◽

Added Benefit ◽

Data Source ◽

High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

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