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With the increase in pipeline usage for fluid transportation, leak detection has become a major concern. More specifically, detecting water leaks has become a pressing challenge to both governmental and industrial stakeholders due to the financial losses it causes as well as the safety concerns associated with it. This issue is further highlighted in industrial and manufacturing environments such as the steel-making process in which a water leak into a furnace can cause a significant explosion that would threaten both the facility and its operators. Therefore, many different water leak detection methods belonging to different types (hardware-in-the-loop-based, simulation-in-the-loop-based, or hybrid) have been proposed in the literature. However, many of these methods either are computationally complex or only suitable for particular applications. Hence, there is a need to develop innovative and novel frameworks that offer effective and efficient water leak detection mechanisms. To that end, this article discusses two different paradigms, namely sensor data fusion and federated learning, that have the potential to further enhance water leak detection methods. Therefore, this article first surveys the different water leak detection methods proposed in the literature along with their merits and limitations. It then describes the sensor data fusion and federated learning paradigms in more detail. Moreover, it presents different research opportunities in which these paradigms can be implemented to offer a more effective and computationally efficient water leak detection framework. |
