Enhancing Pipeline Monitoring: Optimizing Window Size with Monte Carlo Search and CB-AttentionNet

Pipeline monitoring is crucial for preventing severe environmental and economic losses. Therefore, accurate and timely leak detection is essential. Deep learning has become a vital tool for analyzing time-series data to detect pipeline leaks. A key parameter in this analysis is the window size, which refers to the duration of data segments used for processing within the model. Fixed window sizes often fall short when dealing with dynamic and variable-length sequential data. This research advances a probabilistic search framework called Monte Carlo methods to adapt to the dynamic characteristics of pipeline signals. We systematically optimized window sizes ranging from 3 to 90 seconds using a large volume of industrial pipeline data. Our findings indicate that moderate window sizes, particularly between 45 and 60 seconds, provide an effective balance between reducing misclassified leaks and maintaining high training accuracy. Furthermore, our analysis of resource usage and evaluation times demonstrates that the model's performance is efficient and manageable within the constraints of typical operational environments.