Shiddiqi Ary Mazharuddin, Za'in Choiru, Lathifah Artya, Ahmad Tohari, Purwitasari Diana
Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia.
Department of Computer Science and Information Technology, La Trobe University, Australia.
MethodsX. 2024 Feb 10;12:102612. doi: 10.1016/j.mex.2024.102612. eCollection 2024 Jun.
The detection of leaks in time series flow systems is crucial for efficient and integrated industrial processes. This is especially true when daily demand patterns differ, as this results in fluctuations in the snapshots of water consumption that are commonly used as the basis for placing sensors to detect leaks. This paper introduces a novel method in which the genetic algorithm (GA) is applied to find optimal sensor locations and to enhance the accuracy of leak detection in time series flow data. The method consists of two steps. Firstly, the GA is used to identify the optimal sensor locations using a specific fitness function that accounts for flow patterns, system topology, and leak characteristics. The novelty of the proposed method lies in the weighting scheme of the fitness function, which takes into consideration the frequency of events and the magnitude of leaks at potential locations. Secondly, the selected sensor locations are integrated with an advanced time series data analysis to locate leaks. In this technique, the most consistently performing locations are dynamically selected over time, allowing the model to adapt to varying conditions to maintain optimal sensor placement. Experiments were conducted on a simulated time series flow system with known leak scenarios to evaluate the performance of the proposed method. The results demonstrated the superiority of our GA-based sensor placement strategy in terms of leak detection accuracy and efficiency compared to other methods.•We developed a model called GA-Sense for sensor placement strategy by considering flow patterns to maximize leak detection and localization capabilities.•GA-Sense uses time series data to find strategic sensor locations to identify abnormal flow patterns indicative of leaks.•This approach enhances the accuracy and efficiency of leak detection and localization compared to alternative methods.
在时间序列流量系统中及时检测泄漏对于高效和集成的工业过程至关重要。当每日需求模式不同时尤其如此，因为这会导致通常用作放置传感器以检测泄漏的基础的用水快照出现波动。本文介绍了一种新方法，其中应用遗传算法（GA）来找到最佳传感器位置并提高时间序列流量数据中泄漏检测的准确性。该方法包括两个步骤。首先，使用遗传算法通过特定的适应度函数来识别最佳传感器位置，该函数考虑了流量模式、系统拓扑和泄漏特征。所提出方法的新颖之处在于适应度函数的加权方案，该方案考虑了潜在位置的事件频率和泄漏大小。其次，将选定的传感器位置与先进的时间序列数据分析相结合以定位泄漏。在这项技术中，随着时间的推移动态选择性能最稳定的位置，使模型能够适应变化的条件以保持最佳传感器放置。在具有已知泄漏场景的模拟时间序列流量系统上进行了实验，以评估所提出方法的性能。结果表明，与其他方法相比，我们基于遗传算法的传感器放置策略在泄漏检测准确性和效率方面具有优势。
•我们开发了一个名为GA-Sense的模型用于传感器放置策略，通过考虑流量模式来最大化泄漏检测和定位能力。
•GA-Sense使用时间序列数据来找到战略性传感器位置，以识别指示泄漏的异常流量模式。
•与替代方法相比，这种方法提高了泄漏检测和定位的准确性和效率。
