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 User-Centred Scalable Big Data Visualizer for Power Consumption Data in the Electrical Secondary Distribution Network

2020 ◽  
Vol 39 (2) ◽  
pp. 177-197
Author(s):  
Lucina Lawi ◽  
Ellen Kalinga
Keyword(s):  
Big Data ◽  
Smart Grids ◽  
Process Model ◽  
Electrical Power ◽  
Distribution Network ◽  
Point Of View ◽  
Design Activity ◽  
Smart Meters ◽  
Design Process Model ◽  
Secondary Distribution

Establishment of Smart Grids for electrical power has been practised worldwide for the purpose of bringing reliability, security, and efficient management of electrical power networks for enhancing quality service to the society. Apart from the potential aim, smart grid has been a challenge to developing countries, including Tanzania from cost and technology point of view. Due to the use of many smart equipment involved in smart grids like Advanced Metering Infrastructure (AMI) equipped with smart meters and sensors, handling and managing big data has been a challenge. Among the challenges is the issue of visualizing the Big Data due to big volume generated with high velocity. This paper is developing a user-centered scalable big data visualizer for the electrical secondary distribution network by making use of design process model by Akanmu et al. (2017) and design activity framework by McKenna et al. (2014). The approach involves three phases: pre- development, development and post-development phase. The paper reviews several approaches in visualization and demonstrates effective big data visualization. The paper managed to visualize households’ units purchased against power consumed as well as balancing visualization of transformer phases.

THE FOURTH INDUSTRIAL REVOLUTION (INDUSTRY 4.0) A SOCIAL INNOVATION PERSPECTIVE

2018 ◽  
Author(s):  
Klaus Schwab
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Social Innovation ◽  
Holistic Approach ◽  
Socioeconomic Impact ◽  
Fourth Industrial Revolution ◽  
Technological Developments ◽  
Rapid Pace ◽  
Sustainable System ◽  
Industrial Revolutions

The rapid pace of technological developments played a key role in the previous industrial revolutions. However, the fourth industrial revolution (Industry 4.0) and its embedded technology diffusion progress is expected to grow exponentially in terms of technical change and socioeconomic impact. Therefore, coping with such transformation require a holistic approach that encompasses innovative and sustainable system solutions and not just technological ones. In this article, we propose a framework that can facilitate the interaction between technological and social innovation to continuously come up with proactive, and hence timely, sustainable strategies. These strategies can leverage economic rewards, enrich society at large, and protect the environment. The new forthcoming opportunities that will be generated through the next industrial wave are gigantic at all levels. However, the readiness for such revolutionary conversion require coupling the forces of technological innovation and social innovation under the sustainability umbrella.

Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario

2017 ◽  
Vol SED2017 (01) ◽  
pp. 5-7
Author(s):  
Ruchi Jain ◽  
Neelesh Kumar Jain
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Online Education ◽  
Data Protection ◽  
Teaching And Learning ◽  
Relevant Information ◽  
Indian Society ◽  
Digital Learning ◽  
Modern Education ◽  
Indian Scenario

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

Big data/analytics platform for Industry 4.0 implementation in advanced manufacturing context

2021 ◽  
Author(s):  
Renan Bonnard ◽  
Márcio Da Silva Arantes ◽  
Rodolfo Lorbieski ◽  
Kléber Magno Maciel Vieira ◽  
Marcelo Canzian Nunes
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Advanced Manufacturing

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

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