Big data collection and analysis for manufacturing organisations

Pankaj Sharma; David Baglee; Jaime Campos; Erkki Jantunen

doi:10.3934/bdia.2017002

Article Contents

2017, Volume 2, Issue 2: 127-139. Doi: 10.3934/bdia.2017002

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Big data collection and analysis for manufacturing organisations

1.
Department of Mechanical Engineering, Hauz Khas, Indian Institute of Technology Delhi, New Delhi, 110016, India
2.
Faculty of Applied Science, Department of Computing, Engineering and Technology, Industry Centre, Hylton Riverside, Sunderland, UK
3.
Department of Informatics, Linnaeus University, SE-351 95 Växjö, Sweden
4.
VTT Technical Research Centre of Finland, P.O.Box 1000, FI-02044 VTT, Finland

Published: March 2017

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Abstract

Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale benefits for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.

Keywords:

Big data,
CBM,
manufacturing.

Mathematics Subject Classification: 00B10.

Citation:

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Figure 1. A Conceptual architecture of complex data analysis for maintenance decisions (Adapted from Health management of Complex technical systems, [26]

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Figure 2. Old traditional gearbox opened for checking the wear of gear teeth

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Figure 3. Big Data in Asset Management

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