Scientific Journal

Applied Aspects of Information Technology

The task of constructing diagnostic models for nonlinear dynamics objects solved in this work. The reasons for increasing the dimension of the modern diagnostics objects description and related problems of using existing diagnostics methods are considered. The purpose of this work is to increase the accuracy and reliability of nonlinear dynamic objects diagnosing by forming diagnostic models in the conditions of increasing the dimension of the objects description for creating effective tools for automated systems of technical diagnostics. It is offered a broad overview and classification of methods for reducing the dimension space of diagnostic features including nonlinear dynamic objects with continuous characteristics and unknown structure, which can be considered as a “black box”. The forming diagnostic models method of nonlinear dynamic objects based on the combination of spectral characteristics obtained as the result of continuous models transformations: wavelet transformations coefficients and models moments of different orders is proposed. The family of diagnostic models is proposed as combinations of dynamic objects spectral characteristics with weak nonlinearity. The hybrid method of forming diagnostic models based on the combination of spectral characteristics suggested. The method consist of sequential application of feature filtering for forming primary feature space, construction of secondary feature space using the spectral transformations and diagnostic model construction by complete bust of secondary features. It is developed a detailed algorithm for constructing diagnostic models using the proposed hybrid method. The suggested method has been tested on real-life task of diagnosing a non-linear dynamic object – a electric motor. Primary diagnostic model of the electric motor taken on the base of indirect measurements of the air gap between the rotor and the stator of the motor. Diagnostic models constructed by combining the spectral characteristics of continuous models. The diagnostic models family of the switched reluctance motor is offered. The method is demonstrate more independence of the accessibility indicator then existing methods of the diagnostic feature space biulding: the samples, the moment and the coefficients of wavelet transformations of the primary diagnostic models.
1. (2011). Korbicz, J. & Kościelny, J M (Eds , “Modeling, Diagnostics and Process Control: Implementation in the DiaSter System” Berlin, Publ. Springer, 384 p. 
2. Katipamula, S. & Brambley, M. R. (2005). “Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems”. A Review, Part I”. HVAC & R Research, Vol. 11, No. 1, 187 p. 
3. Simani, S., Fantuzzi, C. & Patton, R. J. (2003). “Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques”, Publ. Springer-Verlag, New York, 250 p. 
4. Pavlenko, V. & Fomin, O. (2008). “Methods for Black-Box Diagnostics Using Volterra Kernels”. ICIM 2008: 2nd International Conference on Inductive Modelling, Kyiv, Ukraine, pp. 104-107. 
5. Guyon, I. & Elisseeff, A. (2003). “An introduction to variable and feature selection” J Mach Learn, pp. 1157-82. DOI: 10.1162/153244303322753616. 
6. Gantz, J. & Reinsel, E. (2011). “Extracting Value from Chaos”. IDC’s Digital Universe Study, sponsored by EMC. 
7. Jain, D. & Singh, V. (2018). “Feature selection and classification systems for chronic disease prediction: A review”. Egyptian Informatics Journal, pp. 179-189. DIO: 10.1016/j.eij.2018.03.002. 
8. Fomin, O., Masri, M. & Pavlenko, V. (2016). “Intelligent Technology of Nonlinear Dynamics Diagnostics using Volterra Kernels Moments”. International journal of mathematical models and methods in applied sciences, Volume 10, pp. 158-165. 
9. Aivazian, S. A., Buchhtaber, V. M., Enyukov, I. S. & Meshalkin, L. D. (1989). “Prikladnaya statistika, klassifikatsiya i snizhenie razmernosti”. [Applied Statistics, Classifications and Dimension Reduction], Moscow, Russian Federation [in Russian]. 
10. Tou Julius T. & González Rafael C (1974). “Pattern recognition principles”. AddisonWesley Pub. Co., 377 p. 
11. Fainzilberg, L. S. (2010). “Matematicheskie metodyi otsenki poleznosti diagnosticheskih priznakov”. [Mathematical methods for evaluating the utility of diagnostic features], Kyiv, Ukraine, Education of Ukraine, 152 p. [in Russian] 
12. Tang, Jiliang; Alelyani, Salem & Liu, Huan. (2014). “Feature selection for classification: A review”. Data Classification: Algorithms and Applications. CRC Press, pp. 37-64. 
13. Huan Liu & Motoda, Hiroshi (1998). “Feature Selection for Knowledge Discovery and Data Mining”. The Springer International Series in Engineering and Computer Science, 214 p. 
14. Shardlow, M. (2016). “An analysis of feature selection techniques”. The University of Manchester. 
15. Qu, G., Hariri, S. & Yousif, M. (Sep. 2005). “A new dependency and correlation analysis for features”. IEEE Trans. Knowledge and Data Engineering, Vol. 17, No. 9, pp. 1199-1207. DOI: 10.1109/TKDE.2005.136. 
16. Gopika, N. & Meena kowshalaya M. E. (2018). “Correlation Based Feature Selection Algorithm for Machine Learning”. Proceedings of the International Conference on Communication and Electronics Systems (ICCES 2018), pp. 692-695. 
17. Tran, K. T. & Tran, T. V. (2019). “The application of correlation function in forecasting stochastic processes”. Herald of Advanced Information Technology. Odesa, Ukraine, Science and Technical, Vol. 2, No. 4, pp. 268-277. DOI: 10.15276/hait 04.2019.3. 
18. Kohavi & G. John. (December 1997). “Wrappers for feature selection”. Artificial Intelligence, 97(1-2), pp. 273-324. DOI: 10.1016/S0004-3702(97)00043-X 
19. Max Kuhn & Kjell Johnson. (2013). “Applied Predictive Modeling”. Springer Science+Business Media. New York, 600 p. 20. Mikhaluk, N., Ruvinskaya, V. M. & Shevchuk, I. (2019). “Models based on conformal predictors for diagnostic systems in medicine”. Applied Aspects of Information Technology. Odesa, Ukraine. Science and Technical, Vol. 2 No. 2, pp. 127-137. DOI: 10.15276/aait.02.2019.4. 
21. Amaldi & V. Kann. (1998). “On the approximation of minimizing non zero variables or unsatisfied relations in linear systems”. Theoretical Computer Science, 209, pp. 237-260. DOI: 10.1016/S0304-3975(97)00115-1. 
22. Pavlenko, V. D. & Fomin, A. A. (2000). “Otbor informativnyih sovokupnostey diagnosticheskih parametrov v zadachah mnogoklassovogo raspoznavaniya obrazov”. [Selection criteria for informative sets of features in multiclass recognition]. Proceedings of the OPU, No. 3, pp. 146-150 [in Russian]. 
23. Kumar, V. & Minz, S. (2014). “Feature selection”. SmartCR 2014; Vol. 4(3), pp. 211-29. DOI: 10.6029/smartcr.2014.03.007. 
24. Shahana, A. H. & Preeja, V. (2016). “Survey on feature subset selection for high dimensional data”. In: Circuit, power and computing technologies (ICCPCT), 2016 international conference on. IEEE, p. 1-4. DOI:10.1109/ICCPCT.2016.7530147. 
25. Medvedew, A., Fomin, O., Pavlenko, V. & Speranskyy, V. (2017). “Diagnostic features space construction using Volterra kernels wavelet transforms”. Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 1077- 1081. DOI: 10.1109/IDAACS.2017.8095251. 
26. Grigorenko, S. N., Pavlenko, S. V., Pavlenko, V. D. & Fomin, A. A. (2014). “Information technology of diagnostics of electric motor condition using Volterra models”. EasternEuropean Journal of Enterprise Technologies, Vol 4, No 11(70), pp. 38-43. DOI: 10.15587/1729- 4061.2014.26310. 
27. Giannakis, G. B. & Serpedin, E. (2001). “A bibliography on nonlinear system identification and its applications in signal processing, communications and biomedical engineering”. Signal Processing – EURASIP. Elsevier Science B. V., 81(3), pp. 533-580. 28. Doyle, F. J, Pearson, R. K. & Ogunnaike, B. A. (2001). “Identification and Control Using Volterra Models”. Published Springer Technology & Industrial Arts, 314 p. 
29. Cheng, C. M., Peng, Z. K., Zhang, W. M. & Meng, G. (2016). “Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review”. Mechanical Systems and Signal Processing, pp. 1-25. DOI: 10.1016/j.ymssp.2016.10.029. 
30. Pavlenko S, V., Pavlenko, V. D. (2018). “Deterministic Identification Methods for Nonlinear Dynamical Systems Based on the Volterra Model” Applied Aspects of Information Technology. Odesa, Ukraine. Science and Technical, Vol. 1 No. 1, pp. 11-32. DOI: 10.15276/aait.01.2018.1.

Received 28.01.2020
Received after revision 17.02.2020
Accepted 19.02.2020
Last download:
22 Oct 2021


[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2018.]