## 6 Conclusions  
The article describes the process of building a system to predict valve plate failures in a piston pump. To construct the system, three datasets were developed, describing three different failure states of the pump and the normal operating state. For the failure states, three different plates with varying degrees of degradation were used. During the measurements, various values of pressure, temperature, and flow parameters were recorded at different locations in the system. Using the recorded data, various machine learning models were evaluated along with hyperparameter tuning. These models were assessed in the dataset that describes the moderate state of the valve plate (the Failure 1 dataset) for which the highest number of samples was recorded during physical experiments. Among the five predictive models assessed, the neural network with (100, 10) hidden units organized in two layers achieved the best results. The final best-obtained model was trained on the entire dataset called Failure 1 and then evaluated on datasets representing Failure 2 and Failure 3, allowing for an independent assessment of the model's quality. The results indicate a reasonable accuracy that is 80% for the Failure 2 dataset with the smallest damage, 88% for the Failure 1 dataset with moderate damage, and over 93% for the Failure 3 datasets with the largest damage. So, the level of damage directly corresponds to the prediction accuracy. The feature importance analysis revealed that the most critical parameters are the flow in the leak line, the system output pressure, together with pressure on the leak line and temperature. This information allows the reduction in the number of needed sensors for the construction of a practical predictive maintenance system implementation, significantly reducing costs. The results obtained can be summarized as follows:  
* three classification datasets were developed which represent three levels of valve plate damage,
* the best performance out of the evaluated models was obtained by a neural network consisting of two hidden layers containing 100 and 10 neurons, respectively,
* the system's accuracy depends on the level of damage,
* the most important attributes are the flow in the leak line, the system output pressure, the pressure on the leak line and the temperature,
* the system performance starts to be sensitive to the input parameters when the level of noise in the input data is higher than 5%. Future research is planned to expand the collected data with vibrodiagnostic information, which can further improve the prediction quality of the system. It is also considered to create a digital twin [30; 31] of the pump to simplify the data collection process model of other failure states. Figure 9: The influence of additive noise on prediction model accuracy for different levels of noise standard deviation. **Author Contributions:** Conceptualization, M.R. and M.B. ; methodology, M.R. and M.B. ; software, M.R. and M.B. ; validation, M.B. ; investigation, M.R. ; resources, M.R. ; writing--original draft preparation, M.R. ; writing--review and editing, M.B. ; visualization, M.R. and M.B. ; supervision, M.B. ; project administration, M.B. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Ministry of Science and Higher Education grant number 11040 SDW22030 and the Silesian University of Technology grant number BK-204 RM42024. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The dataset used in the experiments is available on Kaggle: [[https://www.kaggle.com/datasets/mbjunior/valve-plate-failure-prediction-in-hydraulic-pumps](https://www.kaggle.com/datasets/mbjunior/valve-plate-failure-prediction-in-hydraulic-pumps)]([https://www.kaggle.com/datasets/mbjunior/valve-plate-failure-prediction-in-hydraulic-pumps](https://www.kaggle.com/datasets/mbjunior/valve-plate-failure-prediction-in-hydraulic-pumps)) (accessed on 25 June 2024).