Efficiency of operational data processing for radio electronic equipment
The paper deals with the statistical data processing algorithms in operation system of radio electronic equipment. The main purpose is analysis of data processing algorithm efficiency according to the analytical calculations and simulation results. During radio electronic equipment operation failures are possible. These failures affect on the equipment’s technical condition that can deteriorate. In case of condition-based maintenance, it is necessary to detect the time moment of deterioration beginning. Therefore, in this paper the deterioration detection algorithm was developed according to Neyman-Pearson criterion with a fixed sample size. The initial data are times between failures of radio electronic equipment, and these data can be identified by the exponential probability density function. The step-function model was chosen for failure rate change description. To estimate efficiency the operating characteristic was calculated. The simulation based on Monte-Carlo method confirmed the correctness of theoretical calculations.
First published online 22 January 2020
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Dhillon, B. S. (2006). Maintainability, maintenance, and reliability for engineers. New York: Taylor & Francis Group. https://doi.org/10.1201/9781420006780
Galar, D., Sandborn, P., & Kumar, U. (2017). Maintenance costs and life cycle cost analysis (492 p.). Boca Raton: CRC Press. https://doi.org/10.1201/9781315154183
Gertsbakh, I. (2005). Reliability theory: with applications to preventive maintenance (219 p.). New York: Springer. https://doi.org/10.1007/978-3-662-04236-6
Goncharenko, A. (2017). Aircraft operation depending upon the uncertainty of maintenance alternatives. Aviation, 21(4), 126–131. https://doi.org/10.3846/16487788.2017.1415227
Goncharenko, A. V. (2018, 20–24 February). Multi-optional hybrid effectiveness functions optimality doctrine for maintenance purposes. In 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) Proceedings (pp. 771–775). Lviv-Slavske, Ukraine. https://doi.org/10.1109/TCSET.2018.8336313
Hryshchenko, Y. V. (2016, 18–20 October). Reliability problem of ergatic control systems in aviation. In IEEE 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) Proceedings (pp. 126–129). Kyiv, Ukraine. https://doi.org/10.1109/MSNMC.2016.7783123
Jones, M. T. (2009). Artificial intelligence: a systems approach (498 p.). Hingham: Jones & Bartlett Learning.
Kuzmenko, N. S., Ostroumov, I. V., & Marais, K. (2018, 16–18 October). An accuracy and availability estimation of aircraft positioning by navigational aids. In IEEE International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) Proceedings (pp. 36–40). Kyiv, Ukraine. https://doi.org/10.1109/MSNMC.2018.8576276
Levin, B. R. (1978). Theory of reliability of radio engineering systems (274 p.). Moscow: Radio (in Russian).
Mironov, A., Doronkin, P., Priklonsky, A., & Kabashkin, I. (2016). Structural health monitoring of rotating blades on helicopters. Aviation, 20(3), 110–122. https://doi.org/10.3846/16487788.2016.1227554
Rausand, M. (2004). System reliability theory: models, statistical methods and applications (458 p.). New York: John Wiley & Sons, Inc.
Silkov, V., & Delas, M. (2015). Approximate assessment of the operational performances of an unmanned aerial vehicle according to its flight data. Aviation, 19(4), 187–193. https://doi.org/10.3846/16487788.2015.1136024
Smith, D. J. (2005). Reliability, maintainability and risk. Practical methods for engineers (365 p.). London: Elsevier.
Solomentsev, O., Zaliskyi, M., & Zuiev, O. (2013, 5–7 June). Radioelectronic equipment availability factor models. In Signal Processing Symposium 2013 (SPS 2013) Proceedings (pp. 1–4). Jachranka Village, Poland. https://doi.org/10.1109/SPS.2013.6623616
Solomentsev, O. V., Melkumyan, V. H., Zaliskyi, M. Yu., & Asanov, M. M. (2015, 13–15 October). UAV operation system designing. IEEE 3rd International Conference on Actual Problems of Unmanned Air Vehicles Developments (APUAVD) Proceedings (pp. 95–98). Kyiv, Ukraine. https://doi.org/10.1109/APUAVD.2015.7346570
Solomentsev, O., Zaliskyi, M., & Zuiev, O. (2016). Estimation of quality parameters in the radio flight support operational system. Aviation, 20(3), 123–128. https://doi.org/10.3846/16487788.2016.1227541
Solomentsev, O., Zaliskyi, M., Kozhokhina, O., & Herasymenko, T. (2017, 12–14 September). Reliability parameters estimation for radioelectronic equipment in case of change-point. In Signal Processing Symposium 2017 (SPSympo 2017) Proceedings (pp. 1–4). Jachranka Village, Poland. https://doi.org/10.1109/SPS.2017.8053676
Taranenko, A. G., Gabrousenko, Ye. I., Holubnychyi, A. G., & Slipukhina, I. A. (2018, 16–18 October). Estimation of redundant radionavigation system reliability. IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) Proceedings (pp. 28–31). Kyiv, Ukraine. https://doi.org/10.1109/MSNMC.2018.8576282
Tartakovsky, A., Nikiforov, I., & Basseville, M. (2015). Sequential analysis. Hypothesis testing and changepoint detection (580 p.). New York: Taylor & Francis Group. https://doi.org/10.1201/b17279
Wang, W., Zhou, Y., Li, C., Wang, H., & Zhang, Y. (2018). Dynamic reliability analysis of a cantilever beam during a deterioration process. Mechanics Based Design of Structures and Machines, 47(1). https://doi.org/10.1080/15397734.2018.1525992
Zaliskyi, M., & Solomentsev, O. (2014, 14–17 October). Method of sequential estimation of statistical distribution parameters. In IEEE 3rd International Conference Methods and Systems of Navigation and Motion Control (MSNMC) Proceedings (pp. 135–138). Kyiv, Ukraine. https://doi.org/10.1109/MSNMC.2014.6979752
Zhyhlyavskyi, А. А., & Kraskovskyi, A. E. (1988). Changepoint detection of random processes in problems of radio engineering (224 p.). St. Petersburg: LU Publishing (in Russian).