Abstract
Modern manufacturing systems use thousands of sensors retrieving information at hundreds to thousands of samples per second. The real time data being generated is mostly used for monitoring the processes and the equipment condition. Data processing techniques applied to this data to detect anomalies and thus applying preventive maintenance have been used in the industry. Currently available technologies which were developed during the last two decade for scanning the Internet and providing computational services, working at very large scale can be re-targeted to fulfil the requirements of maintenance of complex systems. These systems can support storage and processing of current as well as historical data. Ability to access and process these large data sets will lead from preventive to predictive maintenance and eventually to smart manufacturing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19(2):171–209
Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prog-nostics tools and e-maintenance. Comput Ind 57:476–489
Bandyopadhyay D, Sen J (2011) Internet of things: applications and challenges in technology and standardization. Wirel Pers Commun 58(1):49–69
Maletic JI, Marcus A (2000) Data cleansing: beyond integrity analysis. In: Proceedings of the conference on information quality, pp 200–209
Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView, p 112
The Apache Hadoop Project (2009) http://hadoop.apache.org/core/
Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: OSDI, pp 137–150
Jiang D, Ooi BC, Shi L, Wu S (2010) The performance of MapReduce: an in-depth study. PVLDB 3(1)
HDFS https://hadoop.apache.org/docs/r1.2.1/hdfs-design.html-
YARN http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html
Spark http://spark.apache.org/
LaValle S, et al. (2013) Big data, analytics and the path from insights to value. MIT Sloan Manage Rev 21
Mahout http://mahout.apache.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Patwardhan, A., Verma, A.K., Kumar, U. (2016). A Survey on Predictive Maintenance Through Big Data. In: Kumar, U., Ahmadi, A., Verma, A., Varde, P. (eds) Current Trends in Reliability, Availability, Maintainability and Safety. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-23597-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-23597-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23596-7
Online ISBN: 978-3-319-23597-4
eBook Packages: EngineeringEngineering (R0)