Anomaly Detection in Pipeline Operations Using Unsupervised and Semi-Supervised Learning

Purpose: Oil, gas, and water transportation is important through pipeline systems which are susceptible to various anomalies such as structural degradation, malfunctions in operations, and leakages. Older physics-based and rule-based methods of monitoring, despite their interpretability, tend to have low sensitivity, flexibility, and scalability. However, the absence of labeled fault data, increasing operational complexity, and non-stationary pipeline conditions create a critical gap in reliable and scalable anomaly detection solutions for real-world deployment. This study addresses this gap by systematically analyzing data-driven unsupervised and semi-supervised learning approaches and their applicability to pipeline monitoring. Materials and Methods: New developments in unsupervised and semi-supervised learning have made data-driven anomaly detection schemas able to learn typical operational behavior and detect anomalies with little assistance of labeled fault data. This review gives a detailed summary of these methods within pipeline monitoring. Among the methods discussed are distance- and density-based, statistical and subspace methods, and neural network-based methods, including autoencoders and self-organizing maps. Semi-supervised algorithms such as one-class classification and hybrid statistical-learning are also discussed. The review includes the issues of data characteristics, practices of evaluation, interpretability, and real-time implementation. Findings: The study identifies and discusses a variety of unsupervised and semi-supervised learning techniques that can effectively address the challenges faced by traditional monitoring methods in pipeline systems. It highlights how these data-driven methods are able to detect anomalies by learning typical operational behavior with minimal reliance on labeled fault data. The study also covers important considerations like data characteristics, evaluation practices, and the challenges of implementing these methods in real-time environments. Unique Contribution to Theory, Practice, and Policy: This review provides a thorough evaluation of emerging data-driven anomaly detection methods, contributing to the theoretical understanding of how unsupervised and semi-supervised learning can be applied in pipeline monitoring. The study's practical contribution lies in its exploration of real-world applicability, offering insight into methods that can enhance the sensitivity and scalability of anomaly detection in pipeline systems. For policy, the research suggests future directions, including enhanced feature learning, concept drift adaptation, and integration with digital twins, which aim to improve the trustworthiness and efficiency of anomaly detection in pipeline operations.