Leveraging Machine Learning for Predictive Maintenance of Lock and Dam Infrastructure Using Real-World Data

Abstract

Structural health monitoring (SHM) of water resource infrastructure, such as locks and dams, requires innovative approaches due to their complex environments, accessibility challenges, and variable loading conditions. Machine learning (ML) plays a significant role in detecting anomalies and enabling predictive maintenance capabilities. ML provides a data-driven approach to identify potential structural issues before failure occurs. This study enhances an ensemble-based anomaly detection framework by integrating advanced feature extraction techniques and sensor fusion methods to improve the monitoring and assessment of lock and dam health. A timeseries segmentation approach is utilized to divide sensor data into discrete periods, which are then characterized by a set of statistical features. These features serve as inputs to a Principal Component Analysis (PCA), allowing for effective dimensionality reduction. Advanced feature-level fusion is achieved by leveraging multi-channel strain sensor data, leading to improved anomaly detection and system reliability. This technique enables the integration of data from multiple sensors into a unified framework, offering a comprehensive assessment of structural conditions. An ensemble of ML-based anomaly detection methods is applied to recognize deviations in sensor data patterns, providing an early warning system for potential structural concerns. To refine classification accuracy, the anomaly detection system is supplemented with existing models. This hybrid strategy enhances the interpretation of detected anomalies, reducing false positives and improving the reliability of SHM systems. By merging data-driven ML techniques with well-established physics models, the proposed approach offers a more robust method for predictive maintenance in lock and dam infrastructure. The framework is validated using real-world strain sensor data from a lock gate, where an existing crack is a primary concern for the maintenance team. This study presents a flexible and scalable predictive maintenance framework tailored for lock and dam infrastructure. The proposed approach emphasizes the efficient deployment of SHM methods in real-world scenarios, addressing a critical challenge in the field by providing a practical pathway for implementing SHM solutions effectively.