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.

The paper studies the possibility of assessing the separation of mineral raw materials, taking into account the rheology of the mineral slurry. The ores of the Mayskoye deposit were chosen as the object of the study, characterized by a thin impregnation of the valuable component – gold in the host minerals, which determines the use of fine and ultrafine milling. This fact is essential because the presence of a fine grade seriously affects the rheology of the mineral slurry used in subsequent mineral processing stages. This predetermines the necessity to take into account rheological parameters. The research performed provides the development of a methodology for assessing the separation of minerals in the hydrocyclone based on the interpretation of numerical and mathematical modeling data. using the object-oriented programming language Python, a program for calculating empirical coefficients of the rheological equation, theoretically describing the dynamics of internal transformations of the mineral slurry, was developed. Taking into account the process parameters of the laboratory unit with hydrocyclone and ore properties, three concentrations of solids in the mineral slurry were selected, conditionally corresponding to the minimum, average and maximum values. Rheological equations successively composed for three concentrations, i.e., 400, 500, and 700 g/l, made it possible to calculate the critical shear rates corresponding to the maximum dispersion of the mineral slurry in the hydrocyclone flow. Subsequent numerical simulation using Ansys Fluent software, as well as statistical evaluation of the shear rates at different levels of solids content showed that the shear rate profile in the cross-section of the hydrocyclone corresponding to the maximum dispersion of the mineral slurry is obtained at the content of 400 g/l.