Pressure Signal Dataset for Leak Detection in Electro-Pneumatic Systems

This dataset contains pressure time-series signals acquired from an electro-pneumatic system operating under normal conditions and different leak scenarios. The signals were obtained from controlled experiments and simulations, representing distinct operating states such as normal operation, advance and retraction movements, and internal and external leakage conditions. All signals were sampled at a fixed sampling frequency and stored in CSV format. The dataset was designed to support research on condition monitoring, fault diagnosis, and automatic leak detection using machine learning and signal processing techniques. The dataset was used in the development and evaluation of multiple classification models, including linear models, decision trees, ensemble methods, neural networks, and reservoir computing approaches. Feature extraction was performed using the TSFEL library, resulting in a total of 156 features per signal window. This dataset is publicly available to promote reproducibility and further research in intelligent monitoring of electro-pneumatic systems.