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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.

The operation system determines the efficiency of the intended use of radio equipment. The operation system includes radio equipment, documentation, operational resources, personnel, processes, data processing algorithms, etc. The operation process is associated with conditions of priori uncertainty of models, parameters, external factors, etc. The main element for monitoring is technical condition of radio equipment. In a general case, this condition can deteriorate. The problems of technical condition deterioration analysis can be considered in terms of changepoint study. The present paper concentrates on the problem of synthesis and analysis of method for changepoint detection during radio equipment operation. The detection algorithm is constructed based on Neyman–Pearson criterion with sample size accumulation. During algorithm synthesis the thresholds were calculated according to Bellman’s approach. The problem of synthesis and analysis was solved by two methods: analytically and using statistical modelling. The efficiency measures for this algorithm are the probability of correct detection and mean time of decision making. The proposed algorithm has advantages in mean time of decision making compared with CUSUM detection method.

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