Abstract
Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets. The proposed framework implies a progress beyond the state of the art in the development of Big Data technologies in the fields of Knowledge Discovery algorithms from heterogeneous data sources, scalable data structures, real-time communications and visualizations techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bosnjak, Z., Grljevic, O., & Bosnjak, S. (2009). CRISP-DM as a framework for discovering knowledge in small and medium sized enterprises’ data (pp. 509–514). 5th International Symposium on Applied Computational Intelligence and Informatics, 2009. SACI’09, May 28–29, 2009.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., et al. (2000). Crisp-Dm 1.0: Step-by-step data mining guide. SPSS, Inc.
Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big data: Related technologies, challenges and future prospects. Berlin: Springer.
Chiang, L. H., Braatz, R. D., & Russell, E. L. (2001). Fault detection and diagnosis in industrial systems. Berlin: Springer Science & Business Media.
Galar, D., Gustafson, A., Tormos, B., & Berges, L. (2012). Maintenance decision making based on different types of data fusion. Eksploatacja i Niezawodnosc, 14, 135–144.
Galar, D., Thaduri, A., Catelani, M., & Ciani, L. (2015). Context awareness for maintenance decision making: A diagnosis and prognosis approach. Measurement, 67, 137–150.
ISO 2014. (2014). Asset management—Overview, principles and terminology, ISO55000:2014 Corrected version 2014-03-15, IDT.
Kans, M. (2012). Impact of IT procurement on the quality of the maintenance process: Results from a study in Swedish industry. Journal of Quality in Maintenance Engineering, 18(2), 196–215. ISSN 1355-2511.
Kans, M. (2013). IT practices within maintenance from a systems perspective: Study of IT utilisation within firms in Sweden. Journal of Manufacturing Technology Management, 24(5), 768–791.
Kans, M. (2014). A method for quantifying the effects of investments in CMMS. In J. E. Amadi-Echendu (Ed.), Case studies: Towards engineering asset management body of knowledge and standards, University of Pretoria, Graduate School of Technology Management, The 9th World Congress on Engineering Asset Management, October 28–31, 2014, Pretoria, South Africa.
Lomotey, R. K., & Deters, R. (2014). Towards knowledge discovery in big data (pp. 181–191). IEEE 8th International Symposium on Service Oriented System Engineering (SOSE), April 7–11, 2014.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big data: The next frontier for innovation, competition, and productivity.
Nadali, A., Kakhky, E. N., & Nosratabadi, H. E. (2011). Evaluating the success level of data mining projects based on CRISP-DM methodology by a Fuzzy expert system (pp. 161–165). 3rd International Conference on Electronics Computer Technology (ICECT), April 8–10, 2011.
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials, 16(1), 414–454.
Pilcher, G. (2013). Big data continued… Predictive Analytics Times (Online). Available: http://www.predictiveanalyticsworld.com/patimes/big-data-continued/
Price, C., & Price, C. (1999). Computer-based diagnostic systems. Berlin: Springer.
Swedish Transport Administration. (2014). The Swedish Transport Administration Annual Report 2013, Borlänge.
TWPL. (2015). What is ISO 55000? (Online). Available at: http://www.assetmanagementstandards.com/
Zhang, L., & Karim, R. (2014). Big data mining in eMaintenance: An overview. In: U. Kumar, R. Karmin, A. Parida, & P. Tretten (Eds.), eMaintenance, trends in technologies & methodologies, challenges, possibilities and applications. Proceedings of the 3rd International Workshop and Congress on eMaintenance, June 17–18, 2014. Luleå, Sweden: LTU.
Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill Osborne Media.
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
Galar, D., Kans, M., Schmidt, B. (2016). Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining. In: Koskinen, K., et al. Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-27064-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-27064-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27062-3
Online ISBN: 978-3-319-27064-7
eBook Packages: EngineeringEngineering (R0)