This article presents the application of a self-excited acoustic SAS system for non-destructive testing (NDT) for roof-bolt housings in laboratory and real mine conditions. The proposed system with a filtering mechanism was applied to the J64-27 composite anchors. The conducted tests allowed successful confirmation of the usefulness of the system in the detection of rod defects, damage of the mechanism coupling the anchor to the rock mass and testing of the stress state of the anchor itself. The proposed filtering system allowed eliminating the effect of jump change of frequency in the limit cycle of self-excited system. The proposed method is a novel solution for safety diagnostics of bolt housings in mining applications.

This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.

This article presents a new algorithm for recognizing defects and discontinuities. It is a neural classification algorithm of the SVM class used for the vision system in the technological sequence. At the basis of the used method of Support Vector Machines (SVM) lies the concept of decision-making space, which is divided by building boundaries separating objects with different class affiliation, that is, defects and discontinuities. The Support Vector Machines method is supposed to perform classification tasks by constructing in a multidimensional space hyperplane separating cases belonging to different classes.

This article presents a deep learning algorithm for classifying local material inhomogeneities in aluminum alloy castings based on signals recorded using a self-excited acoustic system (SAS). The aim of this study is to evaluate the potential of using acoustic signals generated in a self-excited system to distinguish between defect-free specimens and those containing internal material defects. The tests were conducted on two specimens with the same geometry: a reference specimen without defects and a specimen with internal defects confirmed by X-ray inspection. Measurements were taken at six corresponding measurement points. A preliminary frequency analysis showed that the presence of inhomogeneities affects the system’s acoustic response; however, the nature of these changes depends on the measurement location and does not allow for the use of a single universal decision criterion based solely on the dominant frequency. For this reason, a classification procedure was developed using the time-frequency representation of the signal and the Audio Spectrogram Transformer architecture. The procedure includes SAS signal acquisition, preprocessing, signal transformation into spectrograms, feature extraction using the AST model, and classification of samples as OK or NOK. The proposed algorithm was evaluated using accuracy, precision, recall, F1-score, AUC, and a confusion matrix. The results indicate that the proposed method can support the non-destructive testing of aluminum castings and serves as a basis for the further development of acoustic signal classification algorithms for the detection of localized material defects.