Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research

Digitalisation is argued to increase the efficiency of maintenance activities in a production system. One consequence of digitalisation is data deluge; this allows data analytics methods and technologies to be used. However, the actual data analytical methods and technologies used may differ, thus leading to many scientific papers on this topic. The purpose of our contribution is to find and cluster scientific papers regarding the implemented approaches relevant for use in production maintenance. Our research is based on a broad, systematic literature review consisting of a two-step search approach combined with additional filtering and classification. Based on the search results, we evaluate and visualise the potential impact of data analytics on the subject of maintenance. The results of this study broadly summarise the research activities in production maintenance, whilst indicating that the impact of data analytics will grow further. Specific methodological approaches are clearly favored

[1]  Fang Lee Cooke.  Plant maintenance strategy: evidence from four British manufacturing firms , 2003 .

[2]  Melnned M. Kantardzic.  Big Data Analytics , 2013, Lecture Notes in Computer Science.

[3]  Inderpreet Singh Ahuja,et al.  An evaluation of TPM initiatives in Indian industry for enhanced manufacturing performance , 2008 .

[4]  Inderpreet Singh Ahuja,et al.  An evaluation of TPM implementation initiatives in an Indian manufacturing enterprise , 2007 .

[5]  Hans-Henrik Hvolby,et al.  Maintenance management models: a study of the published literature to identify empirical evidence , 2015 .

[6]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[7]  Jay Lee,et al.  Intelligent Factory Agents with Predictive Analytics for Asset Management , 2015 .

[8]  F. Cooke.  Implementing TPM in plant maintenance: some organisational barriers , 2000 .

[9]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .

[10]  Sanjay Jain,et al.  Data analytics using simulation for smart manufacturing , 2014, Proceedings of the Winter Simulation Conference 2014.

[11]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[12]  A. Skoogh,et al.  Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030 , 2017 .

[13]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[14]  Chunming Rong,et al.  Predictive Analytics of Sensor Data Using Distributed Machine Learning Techniques , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[15]  Hans-Henrik Hvolby,et al.  A review of the three most popular maintenance systems: how well is the energy sector represented? , 2011 .

[16]  Dursun Delen,et al.  Data, information and analytics as services , 2013, Decis. Support Syst..

[17]  Thomas A. Runkler,et al.  Data Analytics: Models and Algorithms for Intelligent Data Analysis , 2020 .

[18]  Anders Skoogh,et al.  Data-driven algorithm for throughput bottleneck analysis of production systems , 2018 .