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Determining the reliability of a thermal power plant as a whole or in its individual components often requires long and very expensive tests under special operating modes on a very large number of samples or gathering the required exploitation data, which is even more difficult because of the choice of a general mathematical method (different forms of curves which quantitatively define reliability with different failure density functions and the high dependence of such curves on changes in the operating modes of components and environmental conditions). The introduction of approximate calculations, in order to overcome these problems, gives an insight into the basic reliability characteristics of the observed system as a whole, but also insufficiently exact final parameters, due to a whole series of larger or smaller approximations, as well as the inability to take into account all existing influences (development of new technologies, specifics newly developed disorders, etc.). Calculating the reliability of a complex system is only the first initial phase of verifying quantitative characteristics, that is, the hypothesis itself that we have more or less confidence in. Their final acceptance or rejection is a verification of reliability through the control of certain quantitative system indicators for the given technical conditions of operation. For these reasons, alternative terms are often used to verify reliability in the literature, such as reliability control or hypothesis testing. Designing a reliability model, through the application of simulation methods, to select the best parameters for the functioning of components and systems as a whole, in technological terms, should be supported by appropriate experimental methods (using collected data and stored data from the past). This paper provides an analysis of the application of the Markov process to assess the reliability of a complex thermal power system, with the aim of scheduling appropriate decisions on maintenance actions based on the required level of reliability. The optimum timing of replacement / repair of parts of a complex thermal power system is defined before its failure or the need to act correctively. Also, these models serve to provide a level of reliability by carrying out adequate maintenance actions on complex units within the thermal power plant.

In studies of population variability, particular attention has to be paid to the selection of a representative sample. The aim of this study was to assess the size of the new representative sample on the basis of the variability of chemical content of the initial sample on the example of a whitebark pine population. Statistical analysis included the content of 19 characteristics (terpene hydrocarbons and their derivates) of the initial sample of 10 elements (trees). It was determined that the new sample should contain 20 trees so that the mean value calculated from it represents a basic set with a probability higher than 95 %. Determination of the lower limit of the representative sample size that guarantees a satisfactory reliability of generalization proved to be very important in order to achieve cost efficiency of the research. [Projekat Ministarstva nauke Republike Srbije, br. OI-173011, br. TR-37002 i br. III-43007]