Approach to the reference database development for processing abnormal signals of tensometric systems
DOI:
https://doi.org/10.15587/1729-4061.2015.44166Keywords:
time series, fuzzy logic, knowledge base, abnormality classification, tensometryAbstract
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.
References
- 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
- 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
- Jarushkina, N. G. (2004). Osnovy teorii nechetkih i gibridnyh sistem. Moscow: Finansy i statistika, 320.
- Borisov, V. V., Kruglov, V. V., Fedulov, A. S. (2007). Nechetkie modeli i seti. Moscow: Gorjachaja linija – Telekom, 284.
- Rotshtejn, A. P. (1999). Intellektual'nye tehnologii identifikacii: nechetkaja logika, geneticheskie algoritmy, nejronnye seti. Vinnica: UNIVERSUM–Vinnica, 320.
- 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).
- 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).
- 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
- 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).
- Afanas'eva, T. V. (2013). Modelirovanie nechetkih tendencij vremennyh rjadov. Ul'janovsk: UlGTU, 215.
- Kovalev, S. M. (2013). Metody mnogoshagovogo predskazanija anomalij v temporal'nyh dannyh. Izvestija JuFU. Tehnicheskie nauki. Tematicheskij vypusk Intellektual'nye SAPR, 7, 185–181.
- 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.
- 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
- 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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2015 Николай Борисович Копытчук, Петр Метталинович Тишин, Игорь Николаевич Копытчук, Игорь Генрикович Милейко
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.