Approach to the reference database development for processing abnormal signals of tensometric systems

Authors

  • Николай Борисович Копытчук Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine
  • Петр Метталинович Тишин Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine
  • Игорь Николаевич Копытчук Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine https://orcid.org/0000-0002-7953-638X
  • Игорь Генрикович Милейко Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2015.44166

Keywords:

time series, fuzzy logic, knowledge base, abnormality classification, tensometry

Abstract

An algorithm for eliminating abnormalities when measuring signals in processes, proceeding under uncertainty was proposed in the paper. A mathematical model for representing an arbitrary signal, the parameters of which are calculated for a set of standard concepts make the basis of the formed reference database. Based on the analysis of the formed signal reference database, the possibility of applying the model for eliminating abnormalities in the current signal was proved. The problem of restoring signals with abnormalities, using the generated reference database was formulated. To solve the problem, the algorithm for eliminating abnormalities when processing the signals, obtained in the tensometric systems was developed. Its characteristic feature is the classification of the plurality of pilot signals using a set of fuzzy features.

Many standard representations of signals without the abnormalities, generated by an expert, have allowed to develop a reference database of signals without the abnormalities that is represented by a set of values of the mathematical model parameters.

The common result is the ability to process abnormal situations, arising in tensometric systems that can not be determined by other methods. 

Author Biographies

Николай Борисович Копытчук, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Doctor of Technical Sciences, Professor, pensioner

Department of Computer intellectual systems and networks

Петр Метталинович Тишин, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Candidate of Physical and Mathematical Sciences, Associate Professor

Department of Computer intellectual systems and networks

Игорь Николаевич Копытчук, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Senior Lecturer

Department of Computer intellectual systems and networks

Игорь Генрикович Милейко, Odessa National Polytechnic University Shevchenko Avenue, 1a, Odessa, Ukraine, 65044

Technical candidate Sciences, Associate Professor

Department of Computer intellectual systems and networks

References

  1. Song, Q. (2003). A note on fuzzy time series model selection with sample autocorrelation functions. Cybernetics and Systems, 34 (2), 93–107. doi: 10.1080/01969720302867
  2. Song, Q., Chissom, B. (1993). Fuzzy time series and its models. Fuzzy Sets and Systems, 54 (3), 269–277. doi: 10.1016/0165-0114(93)90372-o
  3. Jarushkina, N. G. (2004). Osnovy teorii nechetkih i gibridnyh sistem. Moscow: Finansy i statistika, 320.
  4. Borisov, V. V., Kruglov, V. V., Fedulov, A. S. (2007). Nechetkie modeli i seti. Moscow: Gorjachaja linija – Telekom, 284.
  5. Rotshtejn, A. P. (1999). Intellektual'nye tehnologii identifikacii: nechetkaja logika, geneticheskie algoritmy, nejronnye seti. Vinnica: UNIVERSUM–Vinnica, 320.
  6. Chandola, V. (2009). Anomaly Detection: A Survey. The University Of Minnesota, 72. Available at: http://cucis.ece.northwestern.edu/projects/DMS/publications/AnomalyDetection.pdf (Last accessed: 19.04.2014).
  7. Deepthi Cheboli. Anomaly Detection of Time Series (2010). Facility Of The Graduate School Of The University Of Minnesota, 75. Available at: http://conservancy.umn.edu/bitstream/11299/92985/1/Cheboli_Deepthi_May2010.pdf (Last accessed: 20.04.2014).
  8. Salvador, S., Chan, P. (2005). Learning States and Rules for Detecting Anomalies in Time Series. Applied Intelligence, 23 (3), 241–255. doi: 10.1007/s10489-005-4610-3
  9. Wei, L., Kumar, N. (2005). Assumption–Free Anomaly Detection in Time Series. SSDBM’2005. Proceedings of the 17th international conference on Scientifi c and statistical database management, 237–240. Available at: http://alumni.cs.ucr.edu/~ratana/SSDBM05.pdf (Last accessed: 19.04.2014).
  10. Afanas'eva, T. V. (2013). Modelirovanie nechetkih tendencij vremennyh rjadov. Ul'janovsk: UlGTU, 215.
  11. Kovalev, S. M. (2013). Metody mnogoshagovogo predskazanija anomalij v temporal'nyh dannyh. Izvestija JuFU. Tehnicheskie nauki. Tematicheskij vypusk Intellektual'nye SAPR, 7, 185–181.
  12. Kopytchuk N. B., Tishin, P. M., Kopytchuk, I. N., Milejko, I. G. (2015). Algoritm opredelenija anomal'nyh situacij dlja tenzometricheskih sistem. Vіsnik Nacіonal'nogo tehnіchnogo unіversitetu "HPІ" Zbіrnik naukovih prac'. Serіja: Mehanіko–tehnologіchnі sistemi ta kompleksi, 21 (1130), 37–45.
  13. Kopytchuk, N. B., Tishin, P. M., Kopytchuk, I. N., Milejko, I. G. (2015). Construction of set of standards to improve the accuracy of expert assessments. ScienceRise, 4/2(9), 72–76. doi: 10.15587/2313-8416.2015.41579
  14. Kopytchuk, N. B., Tishin, P. M., Kopytchuk, I. N., Milejko, I. G. (2014). Postroenie aproksimmirujushhej nechetkoj zavisimosti, dlja opredelenija parametrov klassifikacii anomalij, nauchnoe izdanie «Innovacii v nauke». Sbornik statej po maaterialam XXXVI mezhdunarodnoj nauchno–prakticheskoj konferencii, 8 (33), 14–22.

Published

2015-06-23

How to Cite

Копытчук, Н. Б., Тишин, П. М., Копытчук, И. Н., & Милейко, И. Г. (2015). Approach to the reference database development for processing abnormal signals of tensometric systems. Eastern-European Journal of Enterprise Technologies, 3(9(75), 13–20. https://doi.org/10.15587/1729-4061.2015.44166

Issue

Section

Information and controlling system