Architectures and Key Points for Implementation of E-maintenance Based on Intelligent Sensor Networks

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Serafeim Katsikas Apostolos Routzomanis Konstantina Mermikli Dimitrios Dimas Christos Koulamas Vasilis Katsouros Christos Emmanouilidis

Abstract

During the past few years industrial predictive maintenance has benefited from new developments in hardware and software systems. A key conclusion is that to maximize results, these systems need to be smarter with learning capabilities. Moreover, wireless sensor networks have led to a new revolution in the field of e-maintenance, offering new possibilities in measurement collection, aiming to empower monitoring with more advanced features. In what way can wireless sensor networks be applied to industrial maintenance? How can novelty detection be implemented on these systems? How can such systems scale up to offer distributed intelligence? This paper presents the WelCOM research program’s approach on the aforementioned matters answering many questions that relate to intelligent sensor systems in the field of e-maintenance and proposing flexible architectures for the implementation of these systems.

How to Cite

Katsikas, S., Routzomanis, A., Mermikli, K., Dimas, D., Koulamas, C., Katsouros, V., & Emmanouilidis, C. (2014). Architectures and Key Points for Implementation of E-maintenance Based on Intelligent Sensor Networks. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1510
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Keywords

machine learning, signal processing, E-Maintenance, WSN, Novelty Detection

References
Bakar, Z.A., Mohemad, R., Ahmad, A., & Mustafa, M. D. (2006), Α Comparative Study for Outlier Detection Techniques in Data Mining. 2006 IEEE Conference on Cybernetics and Intelligent Systems. June 7-9, Bangkok. doi: 10.1109/ICCIS.2006.252287
Emmanouilidis, C. Jantunen, E., & MacIntyre, J. (2006), Flexible Software for Condition Monitoring, incorporating Novelty Detection and Diagnostics. Computers in Industry Journal. Vol.57, pp. 516-527. (ELSEVIER)
Emmanouilidis, C., Katsikas, S., & Giordamlis, C., (2008), Wireless Condition Monitoring and Maintenance Management: A Review and a Novel Application Development Platform. Proceedings of the 3rd World Congress on Engineering Asset Management and Intelligent Maintenance Systems Conference (WCEAM-IMS 2008) 27 – 30 October 2008, Beijing, China, pp. 2030-2041, (SPRINGER).
Emmanouilidis, C., Hunter, A., MacIntyre, J., & Cox, C. (2001). A multi-objective genetic algorithm approach to feature selection in neural and fuzzy modeling. Journal of Evolutionary Optimization, v3(1), pp.1-26.
Fernández, L., Blasco, J., Hernández, J., & Montón, E., (2009), Technologies for Health and Well-being, Book Wireless Sensor Networks in Ambient Intelligence, Univ. Politécnica de Valencia, Technologies for Health and Well-being, Institute ITACA (Ed.)
Giannoulis, S., Koulamas, C., Emmanouilidis, C., Pistofidis, P., & Karampatzakis, D., Wireless Sensor Network Technologies for Condition Monitoring of Industrial Assets. Advances in Production Management Systems International Conference (APMS) 2012
Holmberg, K., Adgar, A., Arnaiz, A., Jantunen, E., Mascolo, J., & Mekid, S. (Eds.). (2010). E-maintenance. London, UK:Springer – Verlag London Limited.
IAEA. (2007). Implementation Strategies and Tools for Condition Based Maintenance at Nuclear Power Plants. Vienna, Austria.
Jain, A.K., Murty, M.N., & Flynn, P.J. (1999). Data Clustering: A Review, ACM Computing Surveys, Vol. 31, (No. 3).
Katsouros, V., Papavassiliou, V., & Emmanouilidis, C. (2013). A Bayesian Approach for Maintenance Action Recommendation, International Journal of Prognostics and Health Management, International Journal of Prognostics and Health Management, Vol 4 (2) 034, pages: 6, 2013.
LDS Inc. (2003), Understanding FFT windows, ANO14 Application Note.
Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, Massachusetts: The MIT Press.
Montoya, A., Restrepo, D. C., & Ovalle, D. A. (2010). Artificial Intelligence for Wireless Sensor Networks Enhancement, Smart Wireless Sensor Networks, Yen Kheng Tan (Ed.), ISBN: 978-953-307-261-6, Rijeka, Croatia, InTech.
Pistofidis, P., Emmanouilidis, C., Koulamas, C., Karampatzakis, D., & Papathanassiou, N. A layered e- maintenance architecture powered by smart wireless monitoring components. 2012 IEEE International Conference on Industrial Technology (p.390-395), March 19-21, Athens, Greece. doi: 10.1109/ICIT.2012.6209969 Schafer, R., (2011), What is a Savitzky–Golay Filter, IEEE Signal Processing Magazine, Vol. 28 (No. 4),152 pages, doi:10.1109/MSP.2010.939665.
Texas Instruments (1999) Additive Improvement of the MSP430 14-bit ADC Characteristic, Application Report.
Texas Instruments (1999) Linear Improvement of the MSP430 14-bit ADC Characteristic, Application Report.
Vlachos, M., Yu, P., & Castelli, V., (2005), On Periodicity Detection and Structural Periodic Similarity, IBM T.J. Watson Research Center, NY.
Section
Technical Papers