Method for Extracting Diagnostic Features of the Facilities Technical Condition in the System for Monitoring

Technical diagnostics of facilities is an urgent problem during its operation. An integral part of the implementation of diagnostic monitoring systems is the development of a decision support system (DSS) based on the analysis of acoustic emission (AE) diagnostic data and machine learning methods. A necessary condition for the application of machine learning methods in the development of DSS is the process of extracting diagnostic features from the AE signal. In the present work, an improved method is proposed for extracting diagnostic features from time series of AE signals. This includes two successive steps. At the first step, the frequency and frequency-time characteristics are calculated in a sliding window of short duration, which describe local changes in the shape and structure of single pulses. At the second step, the resulting matrix of informative features is aggregated by calculating statistical moments of various orders, which makes it possible to effectively detect long-term trends in the AE signal changes emitted by the defect. Verification of the proposed method was carried out on a full-scale control object of the oil tank RVS No. 3 ("NTEK LLC"). Based on the results obtained, a conclusion was made about the effectiveness of the proposed method in the development of diagnostic monitoring systems based on acoustic emission data.