When implementing energy saving measures, the correct choice of thermal insulation materials, the main characteristic of which is the thermal conductivity coefficient, is of key importance. Missing part of the data, which
may occur during investigation of materials under natural conditions, can lead to incorrect determination of the
corresponding characteristic, which negatively affects the effectiveness of the implemented measures and energy
saving. Therefore, reconstruction of the missing data at the stage of preliminary processing of measured signals
to obtain complete and accurate data when determining the thermal conductivity of thermal insulation materials
will enable to avoid this situation. The article presents the results of regression analysis of data obtained during
express control of thermal conductivity of thermal insulation materials based on the local thermal impact method.
Regression models were built for signal reconstruction with 10%, 20% and 30% missing data, using which a relative error of determination the thermal conductivity coefficient of less than 8% was obtained. This is acceptable for
express control of thermal conductivity and indicates the correctness of data restoration in this way. In addition, an
algorithm is provided for determining signal stationarity, which enables to reasonably reduce the duration of each
material with a given level of permissible error.
Keywords: thermal conductivity determination, insulation materials, regression analysis, missing data, data processing