Abstract. Analysis of production equipment sensors readings is carried out and a data aggregation technique is presented to assess the probability of equipment failure. The study relevance is due to the insufficient development of the scientific and methodological apparatus for assessing equipment condition based on big data. In order to form a data set intended for development of a predictive model of the equipment state, a number of features characterizing the state of machine with numerical program control are constructed. Practical significance of the study is related with the possibility of including the generated data set in the production process and using it to save the values of equipment parameters in time and within the established limits, which characterize its ability to perform the required functions in the specified modes and under the necessary conditions of use.

Keywords: data analysis, machine learning, neural networks, production automation, LSTM

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