## 1 Introduction  
There are several maintenance principles for pumping equipment [1, 2]. First, principles based on preventive measures (the so-called condition-based maintenance principles). When the condition of the pumping equipment is monitored, diagnostics and maintenance are performed as needed. Second, there are the schedule-based maintenance principles (principles based on statistics of typical equipment failures). Their analysis predicts what and at what time it is necessary to perform service and technological operation to maintain the pump in operating condition. Third, there are the principles based on reactive measures, when maintenance is performed on the fait accompli of equipment failure or breakdown. Another pump maintenance strategy is to keep some equipment in reserve and replace it reactively as necessary. These measures impose significant costs on the operating company. Not all plants can have the necessary equipment reserve at all process units. There are many developments with recommendations for the introduction and implementation of systems that provide technological maintenance for equipment based on preventive methods [3, 4]. For example, [5] describes a methodology for planning the maintenance of pumps at existing thermal power plants. A Weibull distribution was used to predict future failures based on the current values of the pumping equipment parameters. As a result of the analysis, the optimal maintenance interval for each individual pump type in the pumping system was determined, taking into account system availability to reduce the cost of unnecessary scheduled maintenance. The authors of [6] discussed maintenance planning for pumps at thermal power plants through reliability analysis based on a small amount of data. An approach to developing a preventive maintenance system for rotodynamic pumps that focuses on diagnosing abnormal events related to hydrodynamic operating conditions is presented in [7]. This method is based on the experimental characteristics of the dynamic response of the pump under different loads and operating anomalies. The technique is implemented on a medium-sized centrifugal pump. The results show that a simple spectral analysis of pressure signals captured at the pump inlet or outlet can provide sufficient decision criteria to form the basis for a diagnostic system. The authors of [8] proposed a complete productive maintenance approach based on intelligent machine learning to achieve zero downtime in industrial equipment. Maintenance includes functional checks, maintenance, and the repair and replacement of necessary parts in, for example, equipment, machinery, and buildings. A data-driven approach to minimize the cost of pump operation and maintenance is discussed in [9]. A neural network algorithm is used to model pump performance using data collected from the process. The authors of [10] presented the concept of intelligent forecasting using neural network algorithms and electronic maintenance of centrifugal pumps. However, despite the rather massive number of works with methods for the preventive maintenance of pumps, practice shows that companies still face difficulties in implementing such maintenance. Based on a survey of 98 Swedish companies, it was found in [11] that maintenance personnel still mostly perform repairs when equipment fails rather than preventive maintenance. Similar results were found in the U.S. [12] and Italy [13]. In addition, [14] notes that among the companies that have created preventive maintenance implementation projects, many do not follow systematic processes. Thus, it can be pointed out that there is a gap between the challenges faced by companies that want to implement preventive maintenance and the advanced solutions presented in the literature.