Condition Based Integrity Assessment of Pipelines

2007 ◽  
Author(s):  
Amrita Kumar ◽  
Robert Hannum ◽  
Shawn J. Beard ◽  
Mamdouh M. Salama ◽  
Will Durnie
Keyword(s):  
Asset Management ◽  
Rapid Assessment ◽  
Cost Benefit ◽  
Sensor Data ◽  
Corrosion Monitoring ◽  
Damage State ◽  
Integrity Assessment ◽  
Service Failures ◽  
Pipeline Structures ◽  
Pipeline Failures

The integrity of pipelines transporting hydrocarbon is critical to economy, safety and environment. One of the leading cause of pipeline failures is 3rd party damage during excavation activities, followed by corrosion, which is becoming increasingly significant as the pipeline infrastructure ages. Current inspection techniques for corrosion monitoring may require the pipeline to be shutdown during inspection reducing overall availability and a potential loss of revenue. Structural Health Monitoring (SHM) offers the promise of a paradigm shift from schedule-driven maintenance to condition-based maintenance (CBM) of pipeline structures. Built-in sensor networks integrated with the pipeline can provide crucial information regarding the condition and damage state of the structure. Diagnostic information from sensor data can be used for prognosis of the health of the structure and facilitate informed decision processes with respect to inspection and repair, e.g., repair vs. no repair or replacement. Asset management can be performed based on the actual health and usage of structures, thereby minimizing in-service failures and maintenance costs, while maximizing reliability and readiness. This paper provides an overview on the design of a SHM system for in-situ real-time, rapid assessment of pipeline integrity using a built-in sensor network. Results of a cost-benefit study conducted for the system usage on pipeline structures will also be presented.

Asset Inspection Management System as a Reliable Inspection/ Monitoring Tool

2020 ◽  
Vol 78 (12) ◽  
pp. 1276-1285
Author(s):  
Shibu John A
Keyword(s):  
Asset Management ◽  
Management System ◽  
Balance Sheet ◽  
Integrity Assessment ◽  
Material Evaluation ◽  
The People ◽  
Physical Assets ◽  
Asset Management System ◽  
On Time Delivery ◽  
Capital Intensive

Enterprise asset management (EAM) systems are used by asset owners and/or operators to manage the maintenance of their physical assets. These assets, including equipment, facilities, vehicles, and infrastructure, need maintenance to sustain their operations. An EAM system provides the means to have less unplanned downtime and extended asset longevity, which offers clear business benefits that improve the profit and loss statement and balance sheet. Particularly for capital-intensive industries, like drilling and exploration, the failure of on-time delivery of critical equipment or processes is disruptive and costs nonproductive time and customer satisfaction. Organizations understand these issues and employ an appropriate asset management system to engineer their asset maintenance and management. An EAM system is needed to manage the people, assets/equipment, and processes. EAMs are used to plan, optimize, execute, and track the needed maintenance activities with associated priorities, skills, materials, tools, and information. Similarly, nondestructive testing (NDT) is used as a tool for integrity assessment of assets in drilling and exploration. The main advantage of using NDT is that the item’s intended use or serviceability is not affected. The selection of a specific technique should be based on knowledge and skills that include design, material processing, and material evaluation. Validating the purpose of this paper, we emphasize the importance of optimizing the asset utilization and serviceability to enhance overall efficiency by integrating EAM software that manages assets, the operation management system (OMS) controlling the processes, and asset inspection management systems (AIMSs).

scholarly journals Use of a Novel Resistive Strain Sensor Approach in an Experimental and Theoretical Study Concerning Large Spherical Storage Tank Structure Behavior During Its Operational Life and Pressure Tests

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 525
Author(s):  
Virgil Florescu ◽  
Stefan Mocanu ◽  
Laurentiu Rece ◽  
Robert Ursache ◽  
Nicolae Goga ◽  
...  
Keyword(s):  
Storage Tank ◽  
Structural Integrity ◽  
Real Life ◽  
Fem Simulation ◽  
Strain Sensor ◽  
Sensor Data ◽  
New Approach ◽  
Integrity Assessment ◽  
Operational Life ◽  
Large Spherical

This paper introduces a new method for the use of tensor-resistive sensors in large spherical storage tank equipment (over 12,000-mm diameters). We did an experiment with 19 petroleum or ammonia product sphere-shaped storage tanks with volumes of 1000 and 1800 cubic meters, respectively. The existing literature only contains experiments based on sensors for tanks with diameters no larger than 600 mm. Based on a number of resistive strain sensor measurements on large spherical pressurized vessels regarding structural integrity assessment, the present paper is focused on the comparison between "real-life" obtained sensor data versus finite element method (FEM) simulation results. The present paper is structured in three parts and examines innovative directions: the use of the classic tensor-resistive sensors in a new approach concerning large structural equipment; an original 3D modeling method with the help of the FEM; and conclusions with possible implications on the regulations, design, or maintenance as a result of the attempt of mutual validation of the new methods previously mentioned.

Private firms, public entities, and microeconomic incentives

2013 ◽  
Vol 21 (2) ◽  
pp. 176-197 ◽  
Author(s):  
Barry Keating ◽  
Maryann Keating
Keyword(s):  
Case Studies ◽  
Asset Management ◽  
Cost Benefit Analysis ◽  
Allocative Efficiency ◽  
Cost Benefit ◽  
Public Private Partnerships ◽  
Primary Case ◽  
Content Type ◽  
The Public ◽  
Optimal Output

PurposePublic private partnerships (PPPs) centralize decision making into a hybrid type of firm, consisting of a government entity with a private firm, that is either a profit‐seeking or non‐profit entity, that initiates, constructs, maintains, or provides a service. The PPP model recognizes that both the public and the private sectors have certain comparative advantages in the performance of specific tasks. PPPs, grounded in cost/benefit analysis, have been used in Australia for decades and are presently being introduced in the USA as a form of innovate contracting. This paper aims to evaluate PPPs as a potentially transferable model for the delivery of public services. PPP firms are evaluated in terms of capital asset management, productive and allocative efficiency, transfer of risk between the public and private sectors, rights to the residual, and the public interest. A case study comparison of Fremantle Ports (Australia) and the Indiana Toll Road (USA) is employed to demonstrate PPP design and function.Design/methodology/approachA description and evaluation of public private partnerships (PPP) is presented and two original and primary case studies are reviewed.FindingsA PPP functioning as a monopoly provider of a common pool public asset approximates economic efficiency when user fees cover virtually full cost. Identifying optimal output and quality assessment is more challenging in the case of social goods in which the public goal is subsidy minimization and clients cannot assess quality. Best practices are helpful; they guarantee the PPP process, but not the outcome. All PPPs, in whatever country or industry, are vulnerable to bureaucratic expansion whenever they are given access to subsidized loans underwritten by taxpayers.Originality/valueThe two case studies in this paper are 100 percent original; they were examined in person by the authors, and the managers of the two entities were interviewed in Indiana (USA) and Fremantle, Western Australia.

scholarly journals Detection of insect health with deep learning on near-infrared sensor data

2021 ◽  
Author(s):  
Emily Nicole Bick ◽  
Sam Edwards ◽  
Henrik Hjarvard De Fine Licht
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Optical Sensors ◽  
Near Infrared ◽  
Rapid Assessment ◽  
Sensor Data ◽  
Agricultural Pests ◽  
Monitoring Methods ◽  
Require Time ◽  
Flying Insects

Conventional monitoring methods for disease vectors, pollinators or agricultural pests require time-consuming trapping and identification of individual insects. Automated optical sensors that detect backscattered near-infrared modulations created by flying insects are increasingly used to identify and count live insects, but do not inform about the health status of individual insects. Here we show that deep learning in trained convolutional neural networks in conjunction with sensors is a promising emerging method to detect infected insects. Health status was correctly determined in 85.6% of cases as early as two days post infection with a fungal pathogen. The ability to monitor insect health in real-time potentially has wide-reaching implications for preserving pollinator biodiversity and the rapid assessment of disease carrying individuals in vector populations.

scholarly journals Estimation of monthly Global Horizontal irradiation pan India using spatial interpolation and comparing its deviations from standard dataset

2021 ◽  
Author(s):  
Vijay Bhaskar Chiluveru
Keyword(s):  
Asset Management ◽  
Spatial Interpolation ◽  
Temporal Trends ◽  
Geographical Location ◽  
Sensor Data ◽  
Solar Pv ◽  
Pv Systems ◽  
Statistical Measures ◽  
Interpolation Technique ◽  
Data Driven Approach

<div><div>In the current scenario of increasing demand for solar photovoltaic (PV) systems, the need to predict their feasibility and performance is more than ever. Irradiance of a geographical location almost exclusively determines the generation possible via solar. Hence, accurate irradiance data is required to assess the value of solar PV systems. Emphasizing such need, this paper presents a method of estimating global horizontal irradiance (GHI) using the two dimensional (2-D) spatial interpolation technique. The proposed model is geo-agnostic and can estimate irradiance depending on the geographical range of the input data. This paper also compares the model predictions with a standard irradiation dataset in the industry. This comparison helps in getting insights regarding the spatio-temporal trends in recent times. As part of our asset management, solar PV plants spread all over India have irradiation sensors whose measures are sent to our servers on a real-time basis. This is incorporated into our in-house analytics portal which is developed for operations and monitoring. Thus, the data is organized for each plant with its geographical parameters (latitude and longitude) along with Global Tilted Irradiation (GTI) measured by on ground sensors. T-factors (calculated as function of tilt, azimuth of the site) corresponding to each sensor orientation are also known which are used to obtain Global Horizontal Irradiation (GHI) values. As part of our study, the increasing predominance of solar PV as a renewable source of energy is discussed. This has focused the attention on the need to have quality irradiation data. The above research has been as an endeavour to use a data-driven approach to solve the issue at hand. Hopefully, this work can showcase the power of using data-intensive techniques such as the one shown to solve the many challenges in the energy industry especially those in solar. The model is built using irradiation sensor data pan India and used an effective spatial interpolation technique, kriging, to produce the gap-filled estimates. The statistical measures of estimate error are also mentioned which show impressive accuracy. Heat maps for respective months have also been produced for better visualization of GHI trends. An independent dataset of industrial benchmarking standards is also compared with the estimates to better understand the temporal GHI trends with respect to long-term averaged values. The assessment of this work’s potential is for the industrial community to ascertain as this can have various use cases of immense business value.</div></div>

scholarly journals Valuing Physical and Social Output: A Rapid Assessment of a London Community Garden

2020 ◽  
Vol 12 (13) ◽  
pp. 5452 ◽  
Author(s):  
Victoria Schoen ◽  
Silvio Caputo ◽  
Chris Blythe
Keyword(s):  
Return On Investment ◽  
Rapid Assessment ◽  
Community Gardens ◽  
Cost Benefit ◽  
Integrated Approach ◽  
Community Garden ◽  
True Value ◽  
Local Residents ◽  
Prime Objective ◽  
Minimal Invasion

The value of urban farms and gardens in terms of their potential for supplying a healthy diet to local residents is well known. However, the prime objective of these spaces often differs from one of food production with this being the means by which other outputs are achieved. Valuing these spaces that provide diverse benefits is therefore a complex exercise as any measure needs to incorporate their physical as well as their social outputs. Only through such an integrated approach is the true value of these gardens revealed and the scale of their potential for contributing to health agendas made apparent. Social return on investment studies can be heavily resource dependent and the rapid cost benefit approach advanced here suggests that with limited expertise and minimal invasion of volunteer and beneficiary time and space, a public value return on investment ratio can be estimated relatively rapidly using an ‘off the shelf’ tool. For the food growing area of a London community garden, a return on investment of £3 for every £1 invested is calculated. This demonstrates the contribution that community gardens can make to social wellbeing within cities and justifies a call for further recognition of these spaces in urban planning policy.

Database Enabled Rapid Seismic Vulnerability Assessment of Bridges

2021 ◽  
pp. 036119812110322
Author(s):  
Leslie Bonthron ◽  
Corey Beck ◽  
Alana Lund ◽  
Xin Zhang ◽  
Yenan Cao ◽  
...  
Keyword(s):  
Asset Management ◽  
Vulnerability Assessment ◽  
Seismic Vulnerability ◽  
Dynamic Models ◽  
Rapid Assessment ◽  
Minimal Amount ◽  
Assessment Methodology ◽  
Seismic Vulnerability Assessment ◽  
Moderate Seismicity ◽  
Available Information

As the seismic hazard has been updated for the central U.S., state Departments of Transportation (DOTs) find an increasing need to assess the seismic vulnerability of their bridge network. Traditional methods to perform seismic assessment require developing dynamic models for each bridge. However, this approach requires specialized engineering knowledge and information from structural drawings, and is time-consuming. To streamline this important task, a simplified dynamic modeling procedure is described that leverages readily available information from DOTs’ asset management databases. With a minimal amount of additional data items, the asset management database can be used to identify vulnerable bridges rapidly and with sufficient accuracy for the prioritization of retrofit decisions. A detailed analysis of a 100-bridge sample set identified typical vulnerabilities and established corresponding capacity thresholds. The rapid seismic vulnerability assessment methodology is implemented as an Excel macro-enabled tool for bridge owners and asset managers to rapidly assess the vulnerability of each individual bridge based on current information in the database, and then classify the vulnerability of each individual bridge as low, medium, or high. Current DOT databases used for asset management in regions of low-to-moderate seismicity do require some data items be added for a robust assessment. These data items are identified here and leveraged to demonstrate the method. The rapid assessment methodology presented can be implemented to effectively identify the most vulnerable bridges in a bridge network, thus facilitating a rapid state bridge inventory network assessment to prioritize and inform actions such as maintenance and rehabilitation.

Risk-Informed Preventive Maintenance Optimization

2004 ◽  
Author(s):  
James K. Liming ◽  
James E. Salter
Keyword(s):  
Asset Management ◽  
Preventive Maintenance ◽  
Human Error ◽  
Failure Modes ◽  
Cost Benefit ◽  
Nuclear Safety ◽  
Target System ◽  
Maintenance Optimization ◽  
Pressurized Water Reactor ◽  
Pressurized Water

Past and ongoing electric generating station owner investments in plant information technology (such as database query applications and other client workstation tools) have made it possible for plant staffs to utilize information contained in the work management systems to quickly link equipment failure modes to related preventative maintenance (PM) activities. A typical pressurized water reactor feedwater (FW) system is applied as the “target system” for examples in this paper. This typical FW system is comprised of approximately 3,800 “tag” or “part number” items which in turn represent about 16,300 failure modes. Effective risk-informed asset management (RIAM) of FW preventive maintenance (PM) activities requires these failure modes to be modeled in a plant availability model. In this paper we present development of a process for supporting PM optimization, applying cost-benefit-risk analysis and RIAM tools and techniques. In this preventive maintenance optimization (PMO) process, PM activities are evaluated for their projected impacts on plant profitability and nuclear safety. PM activities (PMs) are “optimized” for desirable impact to help ensure electric utilities maintain or improve upon high levels of nuclear safety and profitability. In this PMO application the level of detail of the target system(s) is enhanced to support plant decision-making at the component failure mode and human error mode level of indenture. Results of case studies in FW system PMO using typical plant data are presented.

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