The paper presents the results of the classification analysis model for structuring of
processed large volumes of heterogeneous diagnostic data about the technical state of complex
equipment in transport development and research. Concept for the description and structuring
of big data is proposed based on the formation of a metadata scheme using logical breakdown
of all technical diagnostic data on the output variable - the technical condition of complex
technical equipment in transport. A functional assessment of the technical condition complex
technical system’s elements in transport is developed based on the application of methods for
assessing structural and functional risks of failures. The article presents the results of assessing
the accuracy of the input data sets classification using created decision trees models to
effectively structuring and presenting the data in order to ensure that the procedures for their
further analysis are performed. As a result of using the developed simulation model of
structuring and presenting large heterogeneous diagnostic data volumes about the state of
complex technical equipment in transport the time costs were reduced and the efficiency of
analytical operations to study data for solving diagnostic problems and predicting complex
system’s technical condition was improved.
