## 1 Introduction  
Condition monitoring is an effective and widely used tool in modern manufacturing. It contributes to improving production productivity and reliability. Condition monitoring of a variety of industrial equipment is used today to ensure that it is possible to detect any problems in time to prevent unplanned downtime and extensive damage.  
Vibrodiagnostics plays a significant role in monitoring the condition of machinery and equipment. Vibration signatures are an attractive parameter for condition monitoring as they are easily measurable and allow up-to-date information about the machine's condition without direct intervention in the production process. By analysing vibration patterns, anomalies such as mechanical damage, misalignment, or component failures can be detected in the machine long before they develop into critical problems [1, 2].  
In addition to the traditional statistical methods of evaluation and processing of such signals, methods or algorithms based on artificial intelligence are also gaining popularity, as they can monitor and evaluate non-linear dependencies of the analysed parameters [3, 4, 5].  
[PERSON] et. all [5] in the study defines several key techniques that are currently used in vibration signal classification, which include Fuzzy Logic [6; 7], Artificial Neural Networks [8; 9; 10; 11; 12; 13], and traditional signal processing techniques [14; 15; 16]. Recent research in this field has also explored the application of various machine learning and AI techniques for vibration-based condition monitoring and fault diagnosis of rotating machinery [17; 18; 19; 20; 21; 22].  
The key advantages of these techniques include improved diagnostic accuracy, reduced analysis effort, and the ability to handle complex, nonlinear relationships in vibration data [18; 19; 20]. A growing trend can be seen with the implementation of models based on various machine learning principles, even in industrial cases [3; 23; 24; 25; 26].  
The diversity of model architectures used is also noticeable. LSTM and recurrent neural networks have been successfully used for RUL prediction [10; 27] and maintenance task scheduling [28]. Deep convolutional neural networks, in addition to the traditional visual data processing domain, have been successfully used for bearing fault detection [29; 30; 31], gear faults [2; 32], and shaft misalignments [19].  
Researchers have also explored the integration of AI-based methods with traditional vibration analysis techniques, such as time-frequency analysis to enhance the diagnosis of rotating machinery under variable speed conditions [33; 34]. These hybrid approaches have improved performance in extracting relevant features and identifying fault signatures [33; 34].  
However, there are still some gaps and challenges in this area of research. Data-driven models typically rely on signal processing, which demands increased computational capacity and can pose significant challenges in real manufacturing environments. Moreover, it is essential to investigate the integration of machine learning with various data sources and domain-specific knowledge to improve the performance and reliability of these techniques [5; 26]. Furthermore, the interpretability and explainability of AI-based vibration diagnosis methods is an area that requires further research [18; 20]. In addition, a more comprehensive investigation is necessary to evaluate the robustness of these techniques in response to environmental and operational changes, as well as to ascertain their applicability to a broader range of rotating machine types [20; 22; 26].