Industrial communication networks are common in a number of manufacturing organisations. The high availability of these networks is crucial for smooth plant operations. Therefore local and remote diagnostics of these networks is of primary importance in determining issues relating to plant reliability and availability. Condition Monitoring (CM) techniques when connected to a network provide a diagnostic system for remote monitoring of manufacturing equipment. The system monitors the health of the network and the equipment and is therefore able to predict performance. However, this leads to the collection, storage and analyses of large amounts of data, which must provide value. These large data sets are commonly referred to as Big Data. This paper presents a general concept of the use of condition monitoring and big data systems to show how they complement each other to provide valuable data to enhance manufacturing competiveness.
|Title of host publication||Proceedings of the 2017 International Conference on Data Mining DMIN'17|
|Editors||Robert Stahlbock, Mahmoud Abou-Nasr, Gary M. Weiss|
|Number of pages||5|
|Publication status||Published - 17 Jul 2017|
|MoE publication type||A4 Article in a conference publication|
|Event||13th International Conference on Data Mining, DMIN'17 - Las Vegas, United States|
Duration: 17 Jul 2017 → 20 Jul 2017
https://csce.ucmss.com/cr/books/2017/ConferenceReport?ConferenceKey=DMI (Full proceedings)
|Conference||13th International Conference on Data Mining, DMIN'17|
|Abbreviated title||DMIN 2017|
|Period||17/07/17 → 20/07/17|
|Other||DMIN-17 is a part of the World Congress in Computer Science Computer Engineering & Applied Computing CSCE'17|
Baglee, D., Gorostegui, U., Jantunen, E., Campos, J., & Sharma, P. (2017). Optimizing Condition Monitoring of Big Data Systems. In R. Stahlbock, M. Abou-Nasr, & G. M. Weiss (Eds.), Proceedings of the 2017 International Conference on Data Mining DMIN'17 (pp. 127-131). CSREA Press.