###### Abstract  
The article introduces datasets representing piston pump failures along with the experimental evaluation of various machine learning classification models. It starts with a detailed description of three classification datasets consisting of three different levels of valve plate damages and signals recorded from sensors used in classical hydraulic systems (pressure, temperature, flow). The obtained datasets consist of 100k (Failure 1), 30k (Failure 2) and 30k (Failure 3) samples and eight attributes. Then a broad range of classifiers are evaluated including three ensemble models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Next, the attribute importance analysis revealed that leak flow, pressure output, pressure of the leak line, and oil temperature are the most important parameters for accurate predictions. Additionally, the research includes a sensitivity analysis of the best classifier to verify the impact of sensor measurements or other noise indicators on the prediction model performance. The analysis indicates a 5% margin of measurement quality.  
piston pump; machine learning; failure prediction; deep learning; predictive maintenance +
Footnote †: journal: Medical Informatics, Silesian University of Technology, 44-100 Gliwice, Poland
system does not generally seem to be a difficult task, collecting data during system failures is practically impossible because companies strive to prevent failures from occurring. There are two solutions to this problem: one is the creation of digital twins [3,4], which through computer simulations allow the creation of any failure states, and the other is conducting experiments on actual equipment but in laboratory conditions.  
This article describes the research and analysis of piston pump damage conducted in laboratory conditions. During the tests, simulations were carried out with the pump operating with a properly functioning valve plate as well as with three different levels of cavitation damage to the valve plate. The research involved replacing one of the components and conducting simulations under various operating conditions of the pump with a load controlled by an electric motor managed via an inverter.  
In the further part of the study, the collected data were transformed into a dataset suitable for evaluation by machine learning methods. Using these prepared data, a comparison was made of various predictive models including three different ensemble models based on trees: Random Forest [5], Gradient Boosted Trees [6], and Rotation Forest [7], the kNN algorithm [8], and a neural network [9].  
The novelty of the manuscript involves:  
* Introducing a dataset representing real measurements of the valve plate failures in different damage conditions
* Performing a broad comparison of various machine learning classifiers for prediction of the state of the valve plate
* Analysis of the feature importance of the best prediction model in order to verify knowledge extracted by the model
* Analysis of the model's sensitivity to the uncertainty of sensor measurements or other noise sources.  
The subsequent sections are organized as follows: The next section describes the current state of knowledge in the field of predicting hydraulic equipment failures, the following Section 4 provides a detailed description of laboratory tests on the pump and the method of constructing the dataset for the evaluation of machine learning models. Section 5 presents the results obtained along with the discussion of the performance of the models evaluated. It also includes an analysis of the significance of individual attributes. The final section summarizes the conducted research and suggests directions for future work.