Aerospace Technic and Technology

The main directions of improving the processes of life cycle management of power and power plants based on gas turbine engines are based on the strategy of operation according to the technical state. The very concept of a technical condition and the need to assess the tendencies of its permissible change determine the use of applied statistics methods to establish such tendencies - trends. Trend analysis has now emerged as an independent direction in applied statistics due to the specifics of the research object and the importance of applied applications. The problematic issue of the implementation of the strategy for the operation of gas turbine engines according to the technical condition is the reasonable choice of such criteria and methods for determining the trend, which are most consistent with the objects of diagnostics. One of the most important tasks of time series analysis is to substantiate the statistical model of data generation. Such a model, or their combination, should adequately reflect the change in the properties of an object during long-term operation, take into account the features of power plants for aviation and ground use, as well as the experience of use according to the operation manual. The next task is to select and analyze the trend and randomness criteria in relation to the time series, consisting of the parameters for registering the technical state of gas turbine engines. The specificity of trend criteria lies in the fact that at a given level of significance, the hypothesis about the randomness of the time series can only be refuted. The study of an alternative encounters significant difficulties, since the presence of a trend turns a time series into a non-stationary random process. At the same time, the practice of application requires a rational combination of errors of the first (false alarm) and second (trend skipping) kind. Operation by technical condition creates the prerequisites for the use of modern trend analysis methods, which allow dividing the time series into components in accordance with the accepted statistical model of data generation. From the group of such methods, it is advisable to single out the methods of orthogonal decomposition (factor analysis, the method of principal components), since they allow making a predictive assessment of the development of a trend. However, the proposed methods are predominantly scalar in nature, while the initial data on the technical condition of gas turbine engines are multidimensional time series of output parameters related to each other. Comparative analysis of the criteria and methods for determining the trend in the time series of registration parameters is of great practical importance, since it allows increasing the reliability of statistical conclusions about the technical condition of gas turbine engines.

diagnostics; technical condition; gas turbine engine; time series; statistical model; trend criteria

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