Integration of Unsupervised and Supervised Machine Learning Models for Single/Multiple Leak Detection and Localization Using Minimal Sensor Data

The integrity of gas pipelines, both offshore and onshore, is critical for ensuring safe and efficient energy transportation. Leaks in these pipelines can lead to catastrophic environmental damage, economic losses, and safety hazards. Traditional leak detection methods often rely on extensive sensor networks and complex algorithms, which can be costly and challenging to implement, especially in remote or inaccessible locations. There is a pressing need for reliable, cost-effective leak detection systems that utilize minimal sensor data without compromising accuracy. Recent advancements in machine learning offer promising solutions to address these challenges by leveraging data-driven models for anomaly detection and localization.
This research introduces an innovative approach that integrates unsupervised and supervised machine learning models to detect and localize single and multiple leaks in gas pipelines using minimal sensor data—specifically, inlet and outlet mass flow rates and pressures. The novelty of this work lies in its hybrid methodology that combines the strengths of unsupervised learning for anomaly classification with supervised learning for precise leak localization and quantification.

An unsupervised model, such as a Gaussian Mixture Model (GMM), is employed for classification purposes. The GMM is chosen due to its capability to model complex data distributions and identify underlying patterns without prior labeling, making it ideal for distinguishing between normal operation, single leaks, and multiple leaks. Upon detecting an anomaly, a supervised model—implemented using a Random Forest Regressor—is activated to estimate the sizes and locations of the leaks accurately. This two-tiered approach ensures that leaks are not only detected but also precisely located and quantified, facilitating prompt and effective response measures.