## 3 Prediction Hybrid Method  
Traditional predictive methods, such as statistical models, may not always effectively handle the complex relationships between data and the technical state of a TO. Therefore, machine learning methods are increasingly being employed as they can uncover hidden patterns and dependencies in the data.  
Machine learning methods used in a predictive model can be based on classification and regression methods. Classification allows for identifying faults and planning maintenance, with example methods being decision trees, SVM, k-NN, random forests, and neural networks. Regression predicts numerical values, enabling optimal resource management. Example regression methods include linear regression, logistic regression, SVM, random forests, and neural networks. These methods support precise forecasts and appropriate corrective or maintenance actions to ensure the reliability and efficiency of technical objects.  
To increase the effectiveness of predicting the technical state of a TO, a hybrid model has been developed, combining at least two predictive models. Such an approach allows for leveraging the advantages of both methods and achieving synergy, resulting in more accurate forecasts.  
The benefits of employing a hybrid model are numerous. Firstly, by combining different predictive methods, it is possible to incorporate different aspects and perspectives in a data analysis, leading to higher quality predictions. Secondly, a hybrid model may be more resilient to errors and disturbances, as errors from one model can be compensated for by the other. Additionally, the use of a hybrid model can contribute to cost reduction in repairs and maintenance through better planning and optimal resource utilization.  
This section presents a detailed description and analysis of the hybrid method for predicting the technical state of a TO. The individual stages of the developed method are discussed and the results of experiments and comparisons with other approaches are presented. The results confirm the effectiveness of the hybrid method and its advantages over individual predictive methods.  
The research hypothesis was defined as follows:  
\"Combining two different AI methods can lead to achieving predictive results that more accurately correspond to real outcomes than using each predictive method separately.\"  
In our research, we use the MATLAB software (version 23.2) for simulation, and Tableau for the data analysis and results visualization. Simulations allow the modeling of real-life situations or processes, enabling the testing of various scenarios and preparation for potential events. Simulation studies facilitate conducting virtual experiments in a controlled and safe environment, without the need for engaging physical resources or costly field experiments. In addition, simulations enable the verification and validation of theoretical assumptions and the comparison of model results with reality. The MATLAB software, when applied, provides tools for rapid prototyping and iterative testing of ideas and algorithms.  
The use of AI in the presented research is justified by the fact that AI can tailor the diagnostic process to specific devices or systems, enabling more precise results and an approach to maintenance. AI enables continuous real-time monitoring of the technical condition of devices, allowing for real-time response to changes. The elimination or reduction of human errors in the diagnostic process improves not only accuracy but also safety.