Contamination diagnosis in water distribution systems (WDS) is critical for ensuring public health and maintaining water quality. However, exact localization of contamination sources is often highly challenging due to sparse sensor deployment, uncertain water flow dynamics, and the ambiguity of contamination signatures. To address this challenge, we propose a novel end-to-end deep learning framework for ranked contamination diagnosis in WDS. Instead of relying on a single-source prediction, the deep learning model outputs a probabilistic ranking of likely contamination source nodes, enabling top- $k$ diagnosis strategies under uncertainty. A unified classification layer jointly performs contamination detection and source localization, allowing the model to operate in an end-to-end fashion. Extensive experiments on a benchmark WDS demonstrate that the proposed framework achieves strong top- $k$ diagnosis accuracy and remains robust across varying sensor configurations. This work offers a practical and scalable approach to contamination diagnosis in WDS, supporting more informed and effective decision-making in real-world operations. The code is available at: https://github.com/Xiaohan-Chen/ST-GAT.
This paper presents a benchmark for fault detection and isolation (FDI) in floating offshore wind farms, addressing the lack of standardized evaluation frameworks for this emerging technology. Developed using the FOWLTY simulator, the benchmark models a wind farm with seven floating turbines based on the NREL 5MW reference turbine and DeepCWind platform. It incorporates ten diverse wind scenarios (5-23 m/s) and realistic fault conditions, including sensor and actuator faults with variable severity and timing. The benchmark provides sensor measurements with injected noise, actuator reference signals, and evaluation metrics such as false alarm rate (FAR), missed detection rate (MDR), and correct isolation rate. By offering a modular Simulink environment and customizable datasets, this work enables reproducible comparisons of FDI methods while highlighting the unique dynamics of floating offshore systems. The benchmark is publicly available to support research in fault detection and fault-tolerant control for offshore wind energy.
The EvoRoads project develops a connectivity platform that digitalises transport infrastructure and integrates safety assessment services. Leveraging advanced custom-made AI models, EvoRoads analyses infrastructure monitoring data at various geospatial levels, enabling proactive risk warnings and supporting road operators in enhancing safety and operational efficiency. It also defines safety criteria and key performance indicators (KPIs) within the “Safe System” approach. By focusing on data-driven methods, EvoRoads enhances decision making in transportation systems, ensuring the design for reliability and safety while improving maintenance policies. The platform's ability to integrate real-time insights fosters smarter, more responsive infrastructure management, contributing to more efficient and resilient road networks. Co-created with local stakeholders, the EvoRoads use cases are grounded in real-world needs, enhancing the relevance and impact of the solutions. EvoRoads also sets a precedent for future-proof, collaborative urban mobility, advancing the European Vision Zero initiative and providing a blueprint for cities across Europe to enhance their road safety systems. By fostering data-sharing and encouraging transparency, EvoRoads champions an open-data ethos that not only improves safety outcomes but also drives innovation within the broader mobility ecosystem.
The abrupt growth of the world population has raised global concerns about sustainable energy supply. In response, energy policies are continuously evolving to guarantee stability across the energy sector. Stakeholders in the energy supply chain are striving to maintain reliability and balance in the system. Renewable energy sources, because of their low generation cost and sustainability, are increasingly replacing fossil fuel-based power plants. However, their integration introduces significant uncertainty and variability. In this context, accurate forecasting becomes essential for effective planning in renewable-powered microgrids. Reliable forecasts enhance the availability, stability, and overall reliability of microgrids, thereby supporting a resilient and efficient energy supply chain. To address this need, a novel bi-long short-term memory attention algorithm is proposed to enhance the forecasting accuracy of key microgrid parameters that drive power-system decision making. The proposed model demonstrates superior performance over an existing electrical net-load forecasting approach that combines deep neural networks with wavelet transform on the same real-world dataset. Specifically, it achieves a 4% reduction in Mean Absolute Percentage Error and a 2% decrease in Root Mean Square Error. Furthermore, the proposed model accurately tracks the peak and trough points in photovoltaic and wind energy, which is a key factor in forecasting.
This paper presents the design of an interval observer (IO) for systems characterized by nonlinearities that are assumed to fulfill one-sided Lipschitz quadratically innerbounded (OSL-QIB) conditions. This novel approach extends conventional design of IO for Lipschitz systems to OSL-QIB systems with the aim of improving observer design and convergence. Additionally, this new IO design is applied to fault detection, and state estimation which is crucial to guarantee normal process operation. A bioreactor for biomass production and a FHN system are considered as case studies to test the performance of the approach for fault detection and state estimation purposes, respectively.
