Enabling Predictive and Preventive Maintenance using IoT and Big Data in the Telecom Sector

Author(s):  
Tahir Mahmood ◽  
Kamran Munir
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>


2017 ◽  
Vol 13 (4) ◽  
pp. 2039-2047 ◽  
Author(s):  
Jiafu Wan ◽  
Shenglong Tang ◽  
Di Li ◽  
Shiyong Wang ◽  
Chengliang Liu ◽  
...  

Author(s):  
Bitao Yao ◽  
Zude Zhou ◽  
Wenjun Xu ◽  
Yilin Fang ◽  
Luyang Shao ◽  
...  

Large scale machines (LSMs) are always crucial equipments in manufacturing. Maintaining reliability, precision and safety for LSMs is very important. However, LSMs always work under extreme condition and are prone to degradation or failure. Therefore, maintenance is important for them. Compared with preventive maintenance, predictive maintenance is cost-saving. Besides, predictive maintenance is a more sustainable way by reducing failure and enhancing safety. Condition perception is needed in predictive maintenance. Due to the complex structure and large scale of LSMs, the perception data can be characterized as Big Data. Therefore, the storage and processing of Big Data needs to be integrated into maintenance. Considering that LSMs can be distributed all over the word, cloud service can be a proper way to support maintenance in a global environment. In this paper, a framework of service-oriented predictive maintenance for LSMs based on perception Big Data is synthesized to meet those demands. The methodologies are discussed as well. Finally, an industry case is studied to illustrate the implementing of predictive maintenance.


2021 ◽  
Vol 57 (9) ◽  
pp. 6251-6253
Author(s):  
Surabhi Hatagale, Dr. Ramkrishna Manatkar

In fleet management, fleet maintenance is an important exposure to increase availability. Periodic and preventive maintenance is one such crucial aspect which is considered regardless of the practical faults which in sets of the need for repair and replacement cost as well time attached with it. With development in technology IOT and big data have been in talks. With all the data that is being produced predictive maintenance can be performed using this technology. IOT based predictive maintenance can increase fleet availability, stability and efficiency, reduced cost through effective maintenance  planning and eliminate unnecessary maintenance tasks.


Author(s):  
Vishal Mahajan ◽  
Renuka Mahajan ◽  
Richa Misra

This chapter begins with an introduction to the telecom sector, communication using mobile phones, and evolution of wireless communication. It presents the current scenario in this sector, the challenges faced by the telecom industry, especially with ever increasing data network traffic. Finally, the possibilities of harnessing the power of big data analytics, new techniques, and technologies that drive innovation in telecom are presented to help service providers make better decisions and react quickly to threats on the competitive horizon.


ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  

Find Out About 'Big Data' to Track Outcomes


2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.


2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org


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