Informatics and Applications
2018, Volume 12, Issue 3, pp 67-73
METHODS AND TOOLS FOR FAULT DETECTION ON ELEMENTS OF HOUSING AND UTILITY INFRASTRUCTURE
- I. A. Shanin
- S. A. Stupnikov
- V. N. Zakharov
Abstract
The work belongs to the area of development of specific information systems based on the Internet of Things technology. An approach for program implementation of a module intended for detection of faults on elements of housing and utility infrastructure is proposed. The module is considered as a part of an information system aimed at technical maintenance of mentioned elements: condition monitoring, predictive maintenance, fault detection, and reporting. Operation algorithms of module components are described: building of operation models for housing and utility infrastructure elements and fault detection. The approach is applied on a couple of datasets for fault detection, experimental results are presented.
[+] References (12)
- Kovalev, D., I. Shanin, S. Stupnikov, and V. Zakharov.
2018. Data mining methods and techniques for fault detection and predictive maintenance in housing and utility infrastructure. Conference (International) on Engineering Technologies and Computer Science. IEEE. doi: 10.1109/EnT.2018.00016.
- Box, G. E. P., G. M. Jenkins, G. C. Reinsen, and G.M. Ljung. 2015. Time series analysis: Forecasting and control. 5th ed. Wiley. 712 p.
- Cleveland, R.B.,WS. Cleveland, J. E. McRae, and I. Terpenning. 1990. STL: A seasonal-trend decomposition. J. Off. Stat. 6(1):3-73.
- Bengio, Y., and P. Frasconi. 1995. An input output HMM architecture. NIPS Proceedings. MIT Press. 427-434.
- Rabiner, L. R. 2004. A tutorial on hidden Markov models and selected applications in speech recognition. Readings in speech recognition. Eds. A. Waibel and K.-F Lee. San Francisco, CA: Morgan Kaufmann. 267-296.
- Bodik, P., W. Hong, C. Guestrin, S. Madden, M. Paskin, and R. Thibaux. 2004. Intel Lab Data. Intel Berkeley Research lab. Available at: http://db.csail.mit.edu/ labdata/labdata.html (accessed July 16, 2018).
- Sensorscope. 2006. Sensorscope: Sensor networks for en-vironmental monitoring. Lausanne Urban Canopy Ex
periment (LUCE). EPFL. Available at: https://lcav. epfl.ch/page-145180-en.html (accessed July 16, 2018).
- Sharma, A. B., L. Golubchik, and R. Govindan. 2010. Sensor faults: Detection methods and prevalence in real- world datasets. ACM Trans. Sens. Netw. 6(3):23.
- Baljak, V., K. Tei, and S. Honiden. 2013. Fault classification and model learning from sensory Readings - Framework for fault tolerance in wireless sensor networks. 8th Conference (International) on Intelligent Sensors, Sensor Networks and Information Processing. IEEE. 408-413.
- De Bruijn, B., T. A. Nguyen, D. Bucur, and K. Tei. 2016. Benchmark datasets for fault detection and classification in sensor data. 5th Conference (International) on Sensor Networks Proceedings. SCITEPRESS. 185-195.
- De Bruijn, B., T. A. Nguyen, D. Bucur, and K. Tei. 2015. Benchmark datasets for fault detection and classification in sensor data. Available at: http://tuananh.io/datasets/ (accessed July 16, 2018).
- Warriach, E. U., M. Aiello, and K. Tei. 2012. A machine learning approach for identifying and classifying faults in wireless sensor network. 15th Conference (International) on Computational Science and Engineering. IEEE. 618-625.
[+] About this article
Title
METHODS AND TOOLS FOR FAULT DETECTION ON ELEMENTS OF HOUSING AND UTILITY INFRASTRUCTURE
Journal
Informatics and Applications
2018, Volume 12, Issue 3, pp 67-73
Cover Date
2018-08-30
DOI
10.14357/19922264180310
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Internet of Things; data analysis; fault detection; housing and utility infrastructure
Authors
I. A. Shanin  , S. A. Stupnikov , and V. N. Zakharov
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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