Synthetic Wind Speed Data for Tunnel Ventilation System Monitoring

Description

This dataset contains synthetic time series data generated to simulate the operation of a road tunnel ventilation system subject to vehicle induced disturbances. The data were created as part of a study on generative modeling for industrial equipment condition monitoring. The primary goal is to illustrate how latent input effects (specifically, the "piston effect" induced by passing vehicles) can be accounted for in a model that estimates the relationship between the power supplied to the ventilation fans and the measured wind speed in the tunnel. Context and Application: In the simulated tunnel, ventilation fans are used to renew the air continuously. The fans can operate at different speeds; under normal conditions, a higher power input produces a faster wind speed. However, as the fans degrade over time, the same power input results in a lower wind speed. A major challenge in monitoring system performance is that passing vehicles generate transient disturbances (the piston effect) that temporarily alter the measured wind speed. These synthetic data mimic the operational scenario where measurements of wind speed (from anemometers placed at the tunnel entrance and exit) are corrupted by such disturbances. File Descriptions: The dataset comprises six CSV files. Each file contains a sequence of 600 measurements and represents one of two vehicle separation scenarios combined with three noise (disturbance) conditions. The naming convention is as follows: Filename Convention: The files follow the format 25_3_SYN_{separation}_G{gain}.csv, where: {separation} indicates vehicle separation (20 = low rate, 5 = high rate) {gain} indicates the noise level (1.5 = high noise, 1.0 = medium noise, 0.5 = low noise). Data Format and Variables: Each CSV file includes the following columns: time: Sequential time steps (in seconds). u1, u2: The observable input representing the fan setpoint (power supplied to the fans). y1, y2: The measured output, corresponding to the wind speed recorded by the anemometers. y1clean, y2clean: The theoretical wind speed in absence of vehicles. ySSA1, ySSA2: The estimation of y1clean and y2clean from u1, u2, y1, y2 in the accompanying paper. Note: The latent variable representing vehicle entries that cause the piston effect is not directly observable in the files; instead, its impact is embedded in the measured output. Usage: Researchers can use these data files to: - Reproduce the experiments described in the accompanying paper. - Test and benchmark alternative methods for filtering or denoising signals affected by transient disturbances. - Explore generative modeling techniques and inverse problem formulations in the context of equipment condition monitoring. Citation: If you use these data in your research, please cite the accompanying paper as well as this dataset.

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