Centrifugal pumps, as key components of hydraulic systems, play a fundamental role in ensuring the reliable operation of numerous industrial processes in sectors such as energy, chemical production, and oil refining, where uninterrupted equipment performance is of critical importance. Failures of centrifugal pumps can result in substantial financial losses due to costly repairs and unplanned production downtime. This paper presents an innovative approach to diagnosing and detecting faults in centrifugal pumps. The method is based on the application of Motor Current Signature Analysis (MCSA) in combination with automated machine learning (AutoML) technologies. Such an approach enables efficient and highly accurate identification of early signs of equipment malfunction. The experimental study was conducted using an open dataset collected under conditions close to real-world operation. The proposed method achieved a fault detection accuracy of 89 %, which significantly exceeds the performance of the traditional gradient boosting method. This confirms the advantage of a comprehensive model developed through AutoML. Further improvement in diagnostic accuracy was made possible by applying an enhanced Park’s vector transformation to the raw current and voltage data. This approach makes it possible to detect even subtle anomalies in pump operation, thereby strengthening the capability for early fault prediction. The study not only highlights the potential of MCSA as a non-invasive and scalable tool for equipment condition monitoring but also demonstrates the promise of AutoML for technical diagnostics of industrial pumps.

Geomechanical monitoring of the rock mass state is an actively developing branch of geomechanics, in which it is impossible to distinguish a single methodology and approaches for solving problems, collecting and analyzing data when developing seismic monitoring systems. During mining operations, all natural factors are subject to changes. During the mining of a rock mass, changes in the state of structural inhomogeneities are most clearly manifested: the existing natural structural inhomogeneities are revealed; there are movements in discontinuous disturbances (faults); new man-made disturbances (cracks) are formed, which are accompanied by changes in the natural stress state of various blocks of the rock mass. The developed method for evaluating the results of monitoring geomechanical processes in the rock mass on the example of the United Kirovsk mine of the CF AO Apatit allowed to solve one of the main tasks of the geomonitoring system – to predict the location of zones of possible occurrence of dangerous manifestations of rock pressure.