Accelerating data-driven discovery with scientific asset management

2016 ◽  
Author(s):  
Robert E. Schuler ◽  
Carl Kesselman ◽  
Karl Czajkowski
Keyword(s):  
Asset Management ◽  
Data Driven

Structural Health Monitoring System for The Queensferry Crossing

2018 ◽  
Author(s):  
Torben B. Bangsgaard ◽  
Henrik Gjelstrup ◽  
Andrew Scullion ◽  
Paul Faulkner
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Asset Management ◽  
Data Warehousing ◽  
Data Driven ◽  
Health Monitoring System ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Structural Health Monitoring System

The Structural Health Monitoring System (SHMS) for the new Queensferry Crossing cable stayed bridge, Scotland include more than 1500 sensors combined to yield a world leading SHMS for data driven asset management making use of the latests technologies in data processesing and data warehousing.

Automated Real-Time Roadway Asset Inventory using Artificial Intelligence

2020 ◽  
Vol 2674 (11) ◽  
pp. 220-234
Author(s):  
Nima Kargah-Ostadi ◽  
Ammar Waqar ◽  
Adil Hanif
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Asset Management ◽  
Learning Algorithm ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Training Dataset ◽  
Traffic Signs ◽  
Asset Inventory

Roadway asset inventory data are essential in making data-driven asset management decisions. Despite significant advances in automated data processing, the current state of the practice is semi-automated. This paper demonstrates integration of the state-of-the-art artificial intelligence technologies within a practical framework for automated real-time identification of traffic signs from roadway images. The framework deploys one of the very latest machine learning algorithms on a cutting-edge plug-and-play device for superior effectiveness, efficiency, and reliability. The proposed platform provides an offline system onboard the survey vehicle, that runs a lightweight and speedy deep neural network on each collected roadway image and identifies traffic signs in real-time. Integration of these advanced technologies minimizes the need for subjective and time-consuming human interventions, thereby enhancing the repeatability and cost-effectiveness of the asset inventory process. The proposed framework is demonstrated using a real-world image dataset. Appropriate pre-processing techniques were employed to alleviate limitations in the training dataset. A deep learning algorithm was trained for detection, classification, and localization of traffic signs from roadway imagery. The success metrics based on this demonstration indicate that the algorithm was effective in identifying traffic signs with high accuracy on a test dataset that was not used for model development. Additionally, the algorithm exhibited this high accuracy consistently among the different considered sign categories. Moreover, the algorithm was repeatable among multiple runs and reproducible across different locations. Above all, the real-time processing capability of the proposed solution reduces the time between data collection and delivery, which enhances the data-driven decision-making process.

scholarly journals Segmentation and quantification of solar PV losses using data-driven algorithms to help better design and operational monitoring

2021 ◽  
Author(s):  
Vijay Bhaskar Chiluveru
Keyword(s):  
Asset Management ◽  
Plant Performance ◽  
Data Driven ◽  
Solar Pv ◽  
Operational Monitoring ◽  
Pv Systems ◽  
Plant Data ◽  
Increasing Demand ◽  
Using Data ◽  
Plant Components

<div>In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.</div>

scholarly journals Data-driven asset management in urban water pipe networks: a proposed conceptual framework

2021 ◽  
Author(s):  
E. Okwori ◽  
Y. Pericault ◽  
R. Ugarelli ◽  
M. Viklander ◽  
A. Hedström
Keyword(s):  
Data Management ◽  
Conceptual Framework ◽  
Asset Management ◽  
Water Utilities ◽  
Data Driven ◽  
Urban Water ◽  
Water Pipe ◽  
Pipe Network ◽  
Pipe Networks ◽  
Analytical Tools

Abstract Analytical tools used in infrastructure asset management of urban water pipe networks are reliant on asset data. Traditionally, data required by analytical tools has not been collected by most water utilities because it has not been needed. The data that is collected might be characterised by low availability, integrity and consistency. A process is required to support water utilities in assessing the accuracy and completeness of their current data management approach and defining improvement pathways in relation to their objectives. This study proposes a framework to enable increased data-driven asset management in pipe networks. The theoretical basis of the framework was a literature review of data management for pipe network asset management and its link to the coherence of set objectives. A survey to identify the current state of data management practice and challenges of asset management implementation in five Swedish water utilities and three focus group workshops with the same utilities was carried out. The main findings of this research were that the quality of pipe network datasets and lack of interoperability between asset management tools was a driver for creating data silos between asset management levels, which may hinder the implementation of data-driven asset management. Furthermore, these findings formed the basis for the proposed conceptual framework. The suggested framework aims to support the selection, development and adoption of improvement pathways to enable increased data-driven asset management in municipal pipe networks. Results from a preliminary application of the proposed framework are also presented.

scholarly journals Baltic Smart Asset Management – data driven predictive maintenance methods for future

2020 ◽  
Vol 578 ◽  
pp. 012035
Author(s):  
William Hogland ◽  
Christos Katrantsiotis ◽  
Muhammad Asim Ibrahim
Keyword(s):  
Asset Management ◽  
Predictive Maintenance ◽  
Data Driven

scholarly journals Segmentation and quantification of solar PV losses using data-driven algorithms to help better design and operational monitoring

2021 ◽  
Author(s):  
Vijay Bhaskar Chiluveru ◽  
Pooja Chaudhary
Keyword(s):  
Asset Management ◽  
Plant Performance ◽  
Data Driven ◽  
Solar Pv ◽  
Operational Monitoring ◽  
Pv Systems ◽  
Plant Data ◽  
Increasing Demand ◽  
Using Data ◽  
Plant Components

<div>In the current scenario of increasing demand for solar Photo-voltaic (PV) systems, the need to predict their feasibility and monitor performance is more than ever. Although PV systems are known for their reliability, they are not above the damaging effects of their surroundings. Various lossy phenomena affect overall plant performance. In this paper, several of such losses, namely thermal, soiling, module degradation and inverter clipping, are discussed. Algorithms to evaluate these losses are proposed which are data-driven and empirical in nature. This is done as an effort to leverage the analytical capabilities provided by the plant data. The paper also compares the estimated losses with those obtained using the PVsyst simulation. As the latter is an independent industrial standard, it helps in understanding the ground reality of PV performance and insights for better operational monitoring. These insights are of immense business value and are aimed at optimizing performance and thereby revenue. As part of our asset management, all the solar PV plant components have sensors whose measurements are sent to the servers on a real-time basis. This is incorporated into our analytics portal which is used for operations and monitoring. The data used for this study is time-series in nature with a temporal least count of 5 minutes (instantaneous values spaced every 5min throughout the period of data capture). The actual data and its list of parameters is dependent on solar plant capacity and design site. For the reference dataset, a grid-connected solar rooftop PV plant in India was studied and its loss parameters were estimated. The plant components are discussed in the prologue of the results section. Solar PV is such a technology which has been enjoying increasing demand and this market scenario is quite favourable for innovation in energy research. This paper hopes to not only introduce the context of PV losses but also tries to engage the motivation to adopt data-driven and empirical methodologies to understand modern systems. This approach is better in the sense that it only gets better at prediction as time goes by and there is more data. Industrial research such as the above work in critical analysis of PV systems not only helps identify possible limitations but also suggest room for improvement. Since energy generation and project cost are key towards maximizing revenue, these estimation models aimed at predicting PV losses are to be deemed indispensable. As with any estimation, there is no one unique way of hitting the bull’s eye that is to know the exact value. The algorithms proposed above are very much dependent on the quality and quantity of data. However, the comparison between losses estimated using plant data and standard simulation using energy modelling can act as feedback towards improving the design and maintenance of such PV systems.</div>

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