Technical State Prediction of Electronic Systems with Adaptive Parametric Models

This article provides a comparative analysis of existing methods for predicting time series, for the purpose of their applying for early detection of defects and determination of the technical state of complex systems at the current time and in future. The defect forecast and timely assessment of technical state for the subsequent period can improve the readiness and effectiveness of the system functioning as a whole, underscoring the relevance of the proposed approach to building a forecast model. Findings of the research show that the forecast model, built on the basis of the information model, makes it possible to more accurately determine the dynamics of the processes in technical systems, as this model can adequately reproduce the data regularities. If we consider the controlled parameters characterizing the state of the system as a time function, we can solve the problem of predicting changes in system state. This paper proposes an approach to build an adaptive forecasting model of system technical state. This enables us to ensure higher accuracy both of interpolation models construction and extrapolation, given the data significance in the time series using weight coefficients.

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