High-Frequency Modeling of Natural Gas Networks From Low-Frequency Nodal Meter Readings Using Time-Series Disaggregation

Askari, S.; Montazerin, Nader; Zarandi, M. H. Fazel

doi:10.1109/tii.2015.2500883

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…Use cases necessitating high data efficiency 1) Oil / Gas: Large-scale petrochemical plants incorporate dense wireless devices such as RFID tags for machine identification, sensors for large-scale rotational equipment monitoring and fault diagnosis, and employ IIoT technologies for tight and seamless integration between lower layer components, such as sensors and actuators, to the higher level connected with the cloud platforms [19]. In order to ensure the safety of production sites in large petrochemical industries [49], and long interconnected gas networks [50] those sensorial artifacts are positioned around gas pipes, targeting 24/7 monitoring. Data generated by the wireless sensors about parameters and abnormal events are processed for decision making thereby improving production, predicting maintenance and failures for the industrial equipment.…”

Section: H Product-service Systemsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: H Product-service Systemsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…6 A Time Series Reconstruction (TSR) algorithm that uses a regression model and correlated variables to construct an estimate of unobserved time series natural gas consumption data was implemented by Vitullo. 7 Askari et al 8 found a method that handles multiple time series with variable time intervals and tested on a gas network using Lagrange Multipliers method.…”

Section: Section I Introduction To Disaggregationmentioning

confidence: 99%