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COHEN’S CLASS TIME-FREQUENCY DISTRIBUTIONS FOR MEASUREMENT SIGNALS AS A MEANS OF MONITORING TECHNOLOGICAL PROCESSES

https://doi.org/10.17073/0368-0797-2019-4-324-329

Abstract

The article presents and describes Cohen’s class time-frequency distributions which are expedient to use as a mathematical tool that allows to create a convenient – in terms of information content and semantic clarity – visual-graphical representation of the opera ting modes of various technological processes including processes of ferrous metallurgy. It was noted that a controlling process is usually implemented without simultaneous visual monitoring of each scalar (one-dimensional) coordinate that is under control, but the presence of such monitoring is an important condition for the computer-aided controlling of the dynamics of non-stationary technological processes. To eliminate this drawback, it was proposed to perform synchronous monitoring using the multidimensional Cohen’s class time-frequency distributions, when each measurement scalar signal is specifically represented through one of these distributions, for example, the Wigner-Ville distribution. An expression is given for the generalized distribution of Cohen’s class with a distribution kernel and an ambiguity function. This function allows receiving distributions of various types from the maternal function. The most typical representatives of time-frequency distributions forming this class are given with their available  kernels. The possibility of appearance of interference elements, which make it difficult to identify the controlled modes, on a signal distribution map is proved. Case of the formation of virtual elements within the Wigner-Ville distribution representing a two-component one-dimensional signal is considered. Te conditions are explained for the emergence of parasitic elements on the distribution map, obtained, for example, during realizing the process of multi-component feeding the bulk blast furnace charge materials in the production of sintering mixture. An analytical expression is obtained for the Wigner distribution, which displays a multi-component scalar signal and contains the information (useful) and virtual (parasitic) parts of the time-frequency distribution. A link between the number of bulk material feeders available in the feeding devices unit and the number of parasitic (virtual) elements in the Wigner distribution was determined. Using the dosing process as an example, the effect of the noise components propagation in the Wigner distribution is demonstrated. An example is given to illustrate the penetration of noise into the Wigner distribution and appearance of the virtual concentration in it when displaying a signal waveform with a noisy pause and two sections with different frequencies. An expression for the Wigner distribution in the form of a comb function is obtained. The conclusion was made about the parameters of the distribution periodicity and the required sampling frequency of measurement signals.

About the Authors

D. B. Fedosenkov
Siberian Generating Company
Russian Federation

Cand. Sci. (Eng.), Assist. Professor, Head of Asset Management Department

Moscow



A. A. Simikova
Kemerovo State University (KemSU)
Russian Federation

Postgraduate of the Chair “Automation of Production Processes and ACS”

Kemerovo



S. M. Kulakov
Siberian State Industrial University
Russian Federation

Dr. Sci. (Eng.), Professor of the Chair “Automation and Information Systems”

Novokuznetsk, Kemerovo Region



B. A. Fedosenkov
Kuzbass State Technical University named after T.F. Gorbachev
Russian Federation

Dr. Sci. (Eng.), Professor of the Chair “Information and Automated Production Systems”

Kemerovo



References

1. Fedosenkov B.A. Nauchno­tekhnicheskie osnovy sozdaniya i modelirovaniya avtomatizirovannykh sistem upravleniya nepreryvnymi smeseprigotovitel’nymi protsessami. avtoref. dis... doktora tekh. nauk. [Scientifc and technical basis of design and simulation of automated control systems for continuous mixing preparation. Extended Abstract of Dr. Sci. Eng.]. Мoscow, 2005, 55 p. (In Russ.).

2. Cohen L. Time-frequency distributions – A Review. Proceedings of the IEEE. 1989, vol. 77, no. 7, pp. 941–981.

3. Cohen L. Time­frequency analysis. Englewood Cliffs: Prentice Hall, 1995, 299 p.

4. Hlawatsch F. A note on Wigner distribution for fnite duration or band-limited signals and limiting cases. IEEE Trans. Acoust., Speech, Signal Processing. 1988, vol. ASSP-36, pp. 927–929.

5. Ferrando S.E., Doolittle E.J., Bernal A.J., Bernal L.J. Probabilistic matching pursuit with Gabor dictionaries. Signal Processing. 2000, vol. 80, pp. 2099–2120.

6. Cohen L. On a fundamental property of the Wigner distribution. IEEE Trans. Acoust., Speech, Signal Processing. 1987, vol. ASSP-35, pp. 559–561.

7. Cohen L., Posch T. Positive Time–Frequency Distribution Functions. IEEE Trans. Acoust., Speech, Signal Processing. 1985, vol. ASSP-33, pp. 31–38.

8. Cohen L. Wigner distribution for fnite duration or band-limited signals and limiting cases. IEEE Trans. Acoust., Speech, Signal Processing. 1987, vol. ASSP-35, no. 6, pp. 796–806.

9. Debnath L. Recent development in the Wigner-Ville distribution and time-frequency signal analysis. PINSA. 2002, 68 A, no. 1, pp. 35–56.

10. Debnath L. Wavelet transforms and their applications. Birkhauser, Boston, 2002, 565 р.

11. Mallat St. A wavelet tour of signal processing. Academic Press, 2nd Ed., Ecole Politechnique, Paris. Reprinted, 2001. 637 p.

12. Auger F., Chassande-Mottin E. Quadratic time-frequency analysis I: Cohen’s class. In: Time­frequency analysis: concepts and methods. ISTE. 2008 (January), pp. 131–163.

13. Choi H.L., Williams W.J. Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Trans. Acoust., Speech, Signal Processing. 1989, vol. ASSP-37, p. 862–871.

14. Martuganova E.R. Model’ web­servisa po spetsializirovannoi obrabotke dannykh na osnove zhadnykh algoritmov [Model of webservice for specialized data processing based on greedy algorithms]. Мoscow: MGU im. M.V. Lomonosova, 2014, 86 p. (In Russ.).

15. Davis G.M., Mallat S.G., Zhang Z. Adaptive time-frequency decomposition with matching pursuit. In: Proc. SPIE 2242, Wavelet Applications, 402, 1994, pp. 402-413.

16. Mallat S., Zhang Z. Matching pursuit with time- frequency dictionaries. IEEE Transactions on Signal Processing. 1993, vol. 41, no. 12, pp. 3397–3414.

17. Townsend S., Lee B., Jr. Sparse. Approximation and atomic decomposition: considering atom interactions in evaluating and building signal representations. A Dissertation. March 2009, 260 p.

18. Gribonval R., Depalle P., Rodet X., Bacry E., Mallat S. Sound signals decomposition using a high resolution matching pursuit. In: Proc. Int. Computer Music Conf. (ICMC’96). August 1996, pp. 293–296.

19. Boashash B., Touati S., Auger F., Flandrin P., Chassande-Mottin E. etc. Measures, performance assessment and enhancement TFDs. In: Time­frequency signal analysis and processing: a comprehensive reference. Academic Press, 2016, January, pp. 387–452.

20. Sergienko A.B. Tsifrovaya obrabotka signalov [Digital signal processing]. St. Petersburg: Piter, 2006, 751 p. (In Russ.).


Review

For citations:


Fedosenkov D.B., Simikova A.A., Kulakov S.M., Fedosenkov B.A. COHEN’S CLASS TIME-FREQUENCY DISTRIBUTIONS FOR MEASUREMENT SIGNALS AS A MEANS OF MONITORING TECHNOLOGICAL PROCESSES. Izvestiya. Ferrous Metallurgy. 2019;62(4):324-329. (In Russ.) https://doi.org/10.17073/0368-0797-2019-4-324-329

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ISSN 0368-0797 (Print)
ISSN 2410-2091 (Online)