preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.0284.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 1 July 2024 / Approved: 2 July 2024 / Online: 3 July 2024 (12:01:54 CEST)

The article evaluates the use of machine learning methods to predict valve plate failures in hydraulic pumps. To conduct this assessment, a laboratory setup was developed that allowed the creation of a training dataset containing both the normal operating state of the pump and the operating state with three different valve plate damages. Five different predictive models were evaluated, including three composite 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. Additionally, the attribute importance analysis revealed that leak flow, pressure output together with output flow, are the most important parameters for accurate prediction.

piston pump; machine learning; failure prediction; deep learning; predictive maintenance

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

* All users must log in before leaving a comment