scholarly journals Smart Asset Management for Electric Utilities: Big Data and Future

2018 ◽  
pp. 311-322 ◽  
Author(s):  
Swasti R. Khuntia ◽  
Jose L. Rueda ◽  
Mart A. M. M. van der Meijden
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Electric Utilities

scholarly journals Influence of Big Data in managing cyber assets

2019 ◽  
Vol 9 (4) ◽  
pp. 503-514
Author(s):  
Amit Mitra ◽  
Kamran Munir
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Design Methodology ◽  
Insurance Industry ◽  
Manufacturing Industries ◽  
Time Data ◽  
Content Type ◽  
Innovative Leadership ◽  
Data Flows ◽  
Desk Research

Purpose Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for. Design/methodology/approach Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries. Findings By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats. Research limitations/implications Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community. Originality/value There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

scholarly journals Industry 4.0 Contribution to Asset Management in the Electrical Industry

2021 ◽  
Vol 13 (18) ◽  
pp. 10369
Author(s):  
Gabrielle Biard ◽  
Georges Abdul Nour
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Current Model ◽  
Relevant Information ◽  
Point Of View ◽  
Electrical Networks ◽  
Use Of Resources ◽  
Technological Developments

Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.

scholarly journals An Ecosystem Perspective On Asset Management Information

2017 ◽  
Vol 25 (3) ◽  
pp. 150-157
Author(s):  
Lasse Metso ◽  
Mirka Kans
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Sharing ◽  
Asset Management ◽  
Interview Study ◽  
Management Information ◽  
Management Actions ◽  
Railway Industry ◽  
Business Opportunities ◽  
Access To Data

AbstractBig Data and Internet of Things will increase the amount of data on asset management exceedingly. Data sharing with an increased number of partners in the area of asset management is important when developing business opportunities and new ecosystems. An asset management ecosystem is a complex set of relationships between parties taking part in asset management actions. In this paper, the current barriers and benefits of data sharing are identified based on the results of an interview study. The main benefits are transparency, access to data and reuse of data. New services can be created by taking advantage of data sharing. The main barriers to sharing data are an unclear view of the data sharing process and difficulties to recognize the benefits of data sharing. For overcoming the barriers in data sharing, this paper applies the ecosystem perspective on asset management information. The approach is explained by using the Swedish railway industry as an example.

scholarly journals Data analytics and big data in construction project and asset management

2019 ◽  
Vol 9 (4) ◽  
pp. 474-475 ◽  
Author(s):  
Ajibade A. Aibinu ◽  
Fernando Koch ◽  
S. Thomas Ng
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Data Analytics ◽  
Construction Project

scholarly journals A clustering approach for mining reliability big data for asset management

2018 ◽  
Vol 232 (2) ◽  
pp. 140-150 ◽  
Author(s):  
Francesco Cannarile ◽  
Michele Compare ◽  
Francesco Di Maio ◽  
Enrico Zio
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Cost Model ◽  
Reliability Model ◽  
Maintenance Costs ◽  
Switch Point ◽  
Maintenance Strategies ◽  
Clustering Approach ◽  
Asset Managers

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines.

scholarly journals Requirements for Big Data Adoption for Railway Asset Management

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 15543-15564 ◽  
Author(s):  
P. Mcmahon ◽  
T. Zhang ◽  
R. Dwight
Keyword(s):  
Big Data ◽  
Asset Management

Big Data Analytics Maximizes Value from Smart Well Completions

2021 ◽  
Author(s):  
Nasser M. Al-Hajri ◽  
Muhammad Imran Javed ◽  
Akram R. Barghouti ◽  
Hisham I. Al-Shuwaikhat
Keyword(s):  
Big Data ◽  
Economic Analysis ◽  
Asset Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Distribution Functions ◽  
Cumulative Probability ◽  
Data Set ◽  
Valve Opening ◽  
Day To Day Operations

Abstract This paper presents a workflow based on big data analytics to model the reliability of downhole Inflow Control Valves (ICVs) and predict their failures. The paper also offers economic analysis of optimum ICV stroking frequency to maintain valves functionality at the lowest possible cost to the oilfield operator. Installing an ICV in a petroleum well is a costly process and is done by a drilling or workover rig. As such, maintaining a fully functional ICV throughout the lifecycle of a well is important to ensure proper return on investment. ICVs are known to malfunction if not periodically stroked/cycled. The action of stroking ensures that each valve opening is free from obstructing material that would prevent the ICV from operating between one valve opening step to another. When an ICV malfunctions, a costly functionality restoration operation is sometime required without guaranteed results. In other cases, the valve is declared no longer useful and the asset cannot be further utilized due to malfunction. In this paper, an analytical decision making model to predict failures of ICVs is presented that is based on rigorous big data analytics. The model factors in the frequency of stroking before a valve fails. Then, an economic analysis accounting for the CAPEX & OPEX of an ICV is included to optimize the stroking frequency. The utilized techniques include ICV failure and stroking records and classifying the data into pre-defined criteria. Cumulative probability distribution functions are defined for each data set and used to generate failure probability functions. The probability equations are factored into an asset management cost scheme to minimize expected maintenance costs and probability of ICV failure. The results of applying this novel methodology to any smart well clearly showed maximized ICV service life and proper return of investment. The results demonstrate that ICVs lifecycle was prolonged with low maintenance cycling cost. Methodologies similar to the one presented in this paper are true manifestation of the fruitful impact IR4.0 technologies have on oilfields day-to-day operations.

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