Cross-country Pipeline Inspection Data Analysis and Testing of Probabilistic Degradation Models

BACKGROUND The INHERIT (INtersectoral Health and Environment Research for InnovaTion) project has evaluated intersectoral cooperation (IC) in 12 European case studies attempting to promote health, environmental sustainability, and equity through behavior and lifestyle changes. These factors are the concerns of multiple sectors of government and society. Cooperation of health and environmental sectors with other sectors is needed to enable effective action. IC is thus essential to promote a triple win of health, sustainability, and equity. OBJECTIVE This paper describes the design of a qualitative study to gain insights into successful organization of IC, facilitators and barriers, and how future steps can be taken to improve IC in the evaluated case studies. METHODS Each case study was assessed qualitatively through a focus group. A total of 12 focus groups in 10 different European countries with stakeholders, implementers, policymakers, and/or citizens were held between October 2018 and March 2019. Five to eight participants attended each focus group. The focus group method was based on appreciative inquiry, which is an asset-based approach focusing on what works well, why it is working well, and how to strengthen assets in the future. A stepped approach was used, with central coordination and analysis, and local implementation and reporting. Local teams were trained to apply a common protocol using a webinar and handbook on organizing, conducting, and reporting focus groups. Data were gathered in each country in the local language. Translated data were analyzed centrally using deductive thematic analysis, with consideration of further emerging themes. Analyses involved the capability, opportunity, motivation-behavior (COM-b) system to categorize facilitators and barriers into capability, motivation, or opportunity-related themes, as these factors influence the behaviors of individuals and groups. Web-based review sessions with representatives from all local research teams were held to check data analysis results and evaluate the stepped approach. RESULTS Data collection has been completed. A total of 76 individuals participated in 12 focus groups. In December 2019, data analysis was nearly complete, and the results are expected to be published in fall 2020. CONCLUSIONS This study proposes a stepped approach that allows cross-country focus group research using a strict protocol while dealing with language and cultural differences. The study generates insights into IC processes and facilitators in different countries and case studies to filter out which facilitators are essential to include. Simultaneously, the approach can strengthen cooperation among stakeholders by looking at future cooperation possibilities. By providing knowledge on how to plan for, improve, and sustain IC successfully to deal with today’s multisectoral challenges, this study can contribute to better intersectoral action for the triple win of better health, sustainability, and equity. This protocol can serve as a tool for other researchers who plan to conduct cross-country qualitative research. INTERNATIONAL REGISTERED REPORT RR1-10.2196/17323

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Mine Inspection Data Analysis System

Applied Occupational and Environmental Hygiene ◽

10.1080/1047322x.1995.10389042 ◽

1995 ◽

Vol 10 (4) ◽

pp. 323-330 ◽

Cited By ~ 8

Author(s):

Winthrop F. Watts ◽

Douglas R. Parker

Keyword(s):

Data Analysis ◽

Analysis System ◽

Data Analysis System ◽

Inspection Data

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How modern data management software and state of the art inspection gauges improve the efficiency of the pipeline coating inspection process

E3S Web of Conferences ◽

10.1051/e3sconf/201912105001 ◽

2019 ◽

Vol 121 ◽

pp. 05001

Author(s):

David Barnes

Keyword(s):

Data Management ◽

Surface Profile ◽

Test Methods ◽

Data Management System ◽

Pipeline Inspection ◽

Inspection Process ◽

Recent Developments ◽

Speed Up ◽

Inspection Data ◽

The Cost

The inspection of pipeline coating is crucial to the lifetime performance of the pipeline. Inspection during installation of the pipeline and as part of the routine maintenance programme is essential. It is often said that inspection processes save money by ensuring that relevant specifications are achieved but that writing reports for the inspection process cost money. One way to reduce the cost of inspection reporting and to speed up the inspection process is to use a data management system to present the inspection data in a consistent and organised manner. The automation of the reporting process is an important cost saving that allows more time to be allocated to the important task of inspection and the achievement of the coating specification. There have been recent developments in both the design of reporting software and inspection gauges which together make achieving a paperless quality assurance system a reality for all protective coating applications. This paper describes the latest design and operational features of coating thickness gauges, dewpoint meters, surface profile gauges and other related gauges and describes how data can be easily transferred from the memory of these gauges into personal computers and mobile devices by running a dedicated software program for coating inspection data management. The creation of reports combining test results from a broad range of both digital and non-digital test methods will be discussed with particular emphasis on the use of Standard reports and the preparation of pre-formatted report forms.

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Measuring Untapped Revenue Potential in Developing Countries: Cross-Country Frontier and Panel Data Analysis

10.1596/1813-9450-9776 ◽

2021 ◽

Author(s):

Zeljko Bogetic ◽

Dominik Naeher ◽

Raghavan Narayanan

Keyword(s):

Developing Countries ◽

Data Analysis ◽

Panel Data ◽

Panel Data Analysis ◽

Cross Country

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Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach

10.4043/31296-ms ◽

2021 ◽

Author(s):

Subrata Bhowmik

Keyword(s):

Deep Learning ◽

Real Time ◽

Processing Time ◽

Learning Algorithm ◽

Corrosion Monitoring ◽

Digital Twin ◽

Pipeline Inspection ◽

Deep Learning Algorithm ◽

Inspection Data ◽

Pipeline Corrosion

Abstract Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In-Line Inspection images from some previous inspection data sets. During the corrosion monitoring through pipeline inspection, the digital twin system would be able to gather data and, at the same time, process and analyze the collected data. The analyzed data can be used to classify the corrosion type and determine the actions to be taken (develop predictive and prescriptive maintenance strategy). Convolution Neural Network, a well known Deep Learning algorithm, is used in the Tensorflow framework with Keras in the backend is used in the digital twin for corrosion inspection. CNN algorithm will first detect the corrosion and then the type of corrosion based on image classification. The deep-learning network training is done using 4000 images taken from the inspection video frames from a subsea pipeline inspection using ROV. The performances of both the methods are compared based on result accuracy as well as processing time. Deep Learning algorithm, CNN has approximately 81% accuracy for correctly identifying the corrosion and classify them based on severity through image classification. The processing time for the deep-learning method is significantly faster, and the digital twin generates the predictive or prescriptive strategy based on the inspection result in real-time. Deep-learning based digital twin for Corrosion inspection significantly improve current corrosion identification and reduce the overall time for offshore inspection. The inspection data loss due to the communication interference during real-time assessment can be eliminated using the digital twin. The image data can recover the required features based on other features available through the previous inspection. Furthermore, the system can adapt to the unrefined environment, making the proposed system robust and useful for other detection applications. The digital twin makes a recommended decision based on an expert system database during the real-time inspection. The complete corrosion monitoring process is performed in real-time on a cloud-based digital twin. The proposed pipeline corrosion inspection digital twin based on the CNN method will significantly reduce the overall maintenance cost and improve the efficiency of the corrosion monitoring system.

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Post Assessment of Ultrasonic Crack Detection Inline Inspection Data

2008 7th International Pipeline Conference, Volume 2 ◽

10.1115/ipc2008-64357 ◽

2008 ◽

Cited By ~ 1

Author(s):

Lisa Barkdull ◽

Herbert Willems

Keyword(s):

Data Analysis ◽

Crack Detection ◽

Expert Knowledge ◽

Effective Area ◽

Management Program ◽

Metal Loss ◽

Analysis Techniques ◽

Advanced Analysis ◽

Inspection Data ◽

Post Assessment

The information supplied from inline inspection data is often used by pipeline operators to make mitigation and/or remediation decisions based on integrity management program requirements. It is common practice to apply industry accepted remaining strength pressure calculations (i.e. B31G, 0.85 dl, effective area) to the data analysis results from an inline inspection survey used for the detection and characterization of metal loss. Similar assessments of data analysis results from an ultrasonic crack detection survey require expert knowledge in the field of fracture mechanics and, just as importantly, require knowledge to understand the limitations of shear wave ultrasonic technology as applied to an inline inspection tool. Traditionally, crack-like and crack-field features have been classified with a maximum depth distributed over the entire length of the feature; crack-field features also have width reported. In an effort to provide further prioritization, techniques such as “longest length” or “interlinked length” [1] have been employed. More recently, an effort has been made to provide a depth profile of the crack-like or crack-field feature using the ultrasonic crack detection data analysis results. This presentation will discuss the advantages of post assessment of ultrasonic crack detection data analysis results to aid in the evaluation of pipeline integrity and discuss the limitations of advanced analysis techniques. Additionally, the potential for new inline inspection ultrasonic technologies which lend themselves to more accurate data analysis techniques will be reviewed.

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