A simulation-based dataset for anomaly detection in hydrogen blend transport networks

Hydrogen transport involves the safe movement of gaseous hydrogen through industrial pipeline networks, typically between production plants, storage facilities, and distribution centers, and is a key component in the transition toward more sustainable energy sources [1]. Monitoring these networks is essential, as hydrogen is highly flammable and leaks, compressor failures, or delayed component responses can lead to serious accidents, environmental damage, and operational interruptions. Despite the growing interest in this sector, publicly available datasets containing multivariate data on hydrogen transport networks are extremely limited, hindering the development and evaluation of data-driven monitoring methods [[2], [3], [4]]. To address this gap, we present a synthetic dataset simulated using a MATLAB Simscape model of a pipeline segment representative of an industrial network [[5], [6], [7],14]. The dataset includes time-series data from distributed virtual sensors, covering both normal operating conditions and anomalous scenarios such as leaks, compressor failures, and delayed component responses [8,9]. The simulation reproduces transient and steady-state dynamics typical of industrial networks, providing data suitable for the development and evaluation of algorithms for digital twins [10], monitoring, and anomaly detection in hydrogen transport infrastructures [10,11].