## 1 Introduction  
In technical diagnostics, significant emphasis is placed on methods that, apart from the detection of damage and classification of technical conditions, also enable their prediction. Advanced systems for continuous diagnostics of devices enable the monitoring of essential features of these devices in real time (i.e., condition monitoring systems). Such systems consist of measuring sensors, usually connected to a measuring computer or a control module, which make it possible to constantly record the value of diagnostic features of the examined technical object. The ongoing monitoring of the technical condition of an object, then enables the use of methods of predicting the value of the examined features [1]. This, in turn, makes it possible to implement a predictive management model. This optimal management strategy is called predictive maintenance. Therefore, predictive maintenance is based on the extrapolation of acquired data during measurements and setting boundaries for key diagnostic features. Artificial intelligence tools are increasingly being used to extrapolate the obtained data. These are tools that use such areas of knowledge as artificial neural networks, expert systems, fuzzy logic reasoning, and genetic algorithms. Nowadays, thanks to the development of computer science, measurement techniques, and automation, it is possible to build automatic diagnostic systems in which formulation of the assessment of the technical conditions of objects, detection and localization of malfunctions, as well as forecasting the period of correct operation of these objects can take place without or with minimal human participation [2, 3]. Artificial neural networks turn out to be useful in the process of identifying failures. Their proper application also enables forecasting of the processes and phenomena that occur in technical objects and the interactions between these objects. Predictive maintenance usually involves real-time monitoring of the relevant diagnostic features. In the case of vehicle diagnostics, predictive maintenance can be successfully used in monitoring systems and when measurements of diagnostic features are performed only during technical inspections. In this case, the use of predictive maintenance methods seems to be less effective than in the case of constant monitoring of diagnostic features. However, with the help of monthly surveys, it is possible to obtain sufficiently accurate prediction results, which are presented in the research part of this paper. In the case of individual elements of technical objects, many diagnostic features are measured during a standard technical inspection. Therefore, many factors (diagnostic features) can be identified that affect the technical conditions of such objects and the need to replace elements. These factors are often interrelated, and the ability to indirectly measure selected features also allows us to take into consideration those features where the measurements are more complicated. For example, information on the quality of the railway infrastructure cannot be obtained directly, and the quality of the infrastructure has a huge impact on the wear and tear process of individual components of the rail vehicle. However, such information can be obtained indirectly, e.g., based on an analysis of the distance traveled by the vehicle, the service intervals, wear intensity of the tested element, weather conditions, and vehicle speed. The use of artificial intelligence methods makes it possible to easily consider factors that could not be directly taken into consideration. In addition, determining the technical condition of a technical object as well as identifying the reasons for its replacement and potential causes of damage can be treated as a problem of pattern recognition and classification. For this purpose, artificial intelligence methods are the best solution due to the possibility of analyzing very large datasets related to each other indirectly.