Skip to main content

Reliable Condition Monitoring of Telecommunication Services with Time-Varying Load Characteristic

  • Conference paper
  • First Online:
Distributed Computing and Internet Technology (ICDCIT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10722))

Abstract

In general, SLAs (Service-Level Agreements) between TSPs (Telecommunication Service Providers) and their computer system vendors contain grants of penalty demands on the vendors in case of SLA violations. Occasionally, TSPs also cede such rights to their customers. In this case, TSPs behave wisely if they install CMSs (Condition Monitoring Systems) that nonstop supervise all significant KPIs (Key Performance Indicators) of their services and red-flag noticeable service problems. Scientists have researched a variety of concepts for CMSs with machined dynamic thresholds, for instance, to take the material aging of rotary machines into account. Nary such a concept deftly copes with time-based volatility, e.g. telecommunication services that show time-varying load characteristic. This disquisition fills this gap by presenting the requirements, the architecture, and the reliability analysis for an applicable CMS (Condition Monitoring System).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agarwal, D., Kishor, N., Raghuvanshi, A.S.: Flexible threshold selection and fault prediction method for health monitoring of offshore wind farm. IET Wirel. Sens. Syst. 5, 183–192 (2015). https://doi.org/10.1049/iet-wss.2014.0008

    Article  Google Scholar 

  2. Beyaz, S.: Konzeption, Einführung und Integration eines Monitoringsystems in bestehende Netzwerkdienste in einer Krankenhausumgebung. Ph.D. thesis, University of Erlangen-Nuremberg, Erlangen, Bavaria, Germany, June 2010. https://opus4.kobv.de/opus4-fau/files/1260/Dissertation_Beyaz.pdf

  3. Brooks, R., Thorpe, R., Wilson, J.: A new method for defining and managing process alarms and for correcting process operation when an alarm occurs. J. Hazard. Mater. 115(1–3), 169–174 (2004). https://doi.org/10.1016/j.jhazmat.2004.05.040

    Article  Google Scholar 

  4. Case, J.D., Fedor, M., Schoffstall, M.L., Davin, J.R.: A simple network management protocol (SNMP). RFC 1157 (Historic), May 1990. https://www.ietf.org/rfc/rfc1157.txt

  5. Chen, J., Wan, Z., Pan, J., Zi, Y., Wang, Y., Chen, B., Sun, H., Yuan, J., He, Z.: Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain. Mech. Syst. Sign. Process. 6869, 44–67 (2016). https://doi.org/10.1016/j.ymssp.2015.07.022

    Article  Google Scholar 

  6. Dolenc, B., Boškoski, P., Juričić, Ð.: Robust information indices for diagnosing mechanical drives under non-stationary operating conditions. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds.) Advances in Condition Monitoring of Machinery in Non-stationary Operations. ACM, vol. 4, pp. 139–149. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-20463-5_11

    Chapter  Google Scholar 

  7. Gray, J., Siewiorek, D.P.: High-availability computer systems. Computer 24(9), 39–48 (1991). https://doi.org/10.1109/2.84898

    Article  Google Scholar 

  8. International Telecommunication Union: X.700: Management framework for open systems interconnection (OSI) for CCITT applications, September 1992. https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-X.700-199209-I!!PDF-E

  9. Jabłoński, A., Barszcz, T., Bielecka, M., Breuhaus, P.: Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems. Measurement 46(1), 727–738 (2013). https://doi.org/10.1016/j.measurement.2012.09.011

    Article  Google Scholar 

  10. Juričić, Ð., Kocare, N., Boškoski, P.: On optimal threshold selection for condition monitoring. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds.) Advances in Condition Monitoring of Machinery in Non-stationary Operations. ACM, vol. 4, pp. 237–249. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-20463-5_18

    Chapter  Google Scholar 

  11. Kearney, K.T., Torelli, F.: The SLA model. In: Wieder, P., Butler, J., Theilmann, W., Yahyapour, R. (eds.) Service Level Agreements for Cloud Computing, pp. 43–67. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-1614-2_4

    Chapter  Google Scholar 

  12. Kocare, N., Juričić, D., Boškoski, P.: Optimal threshold selection in condition monitoring based on probability of false alarm. In: 23rd International Electrotechnical and Computer Science Conference ERK 2014, pp. 175–178, September 2014. http://erk.fe.uni-lj.si/2014/kocare(optimal)p.pdf

  13. Marhadi, K.S., Skrimpas, G.A.: Using Johnson distribution for automatic threshold setting in wind turbine condition monitoring system. In: Annual Conference of the PHM Society (PHM), vol. 5, pp. 1–13, October 2014. https://www.phmsociety.org/node/1395

  14. Marhadi, K.S., Skrimpas, G.A.: Automatic threshold setting and its uncertainty quantification in wind turbine condition monitoring system. Int. J. Prognostics Health Manage. 6(Special Issue Uncertainty in PHM), 1–15 (2015). https://www.phmsociety.org/node/1571

    Google Scholar 

  15. Pukelsheim, F.: The three sigma rule. Am. Stat. 48(2), 88–91 (1994). https://doi.org/10.2307/2684253

    MathSciNet  Google Scholar 

  16. Straczkiewicz, M., Barszcz, T., Jabłoński, A.: Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions. J. Phys: Conf. Ser. 628(1), 1–8 (2015). https://doi.org/10.1088/1742-6596/628/1/012087

    Google Scholar 

  17. Terry, D.B., Painter, M., Riggle, D.W., Zhou, S.: The Berkeley internet name domain server. Technical report, EECS Department, University of California, Berkeley, CA, USA, May 1984. https://www2.eecs.berkeley.edu/Pubs/TechRpts/1984/CSD-84-182.pdf

  18. Yan, H.C., Zhou, J.H., Pang, C.K.: Cost optimization on warning threshold and non-fixed periodic inspection intervals for machine degradation monitoring. In: IECON 2015–41st Annual Conference of the IEEE Industrial Electronics Society, pp. 1079–1084, November 2015. https://doi.org/10.1109/IECON.2015.7392243

Download references

Acknowledgments

Many thanks to Bettina Baumgartner from the University of Vienna for proofreading this paper!

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Günter Fahrnberger .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fahrnberger, G. (2018). Reliable Condition Monitoring of Telecommunication Services with Time-Varying Load Characteristic. In: Negi, A., Bhatnagar, R., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2018. Lecture Notes in Computer Science(), vol 10722. Springer, Cham. https://doi.org/10.1007/978-3-319-72344-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72344-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72343-3

  • Online ISBN: 978-3-319-72344-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics