Detection of Pipe Ruptures in Shipboard Firefighting Systems Using Machine Learning and Deep Learning Techniques

Sara Ferreno-Gonzalez, Vicente Diaz-Casas, Marcos Miguez-Gonzalez, Carlos G. San-Gabino

In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Neural network models were developed to distinguish between normal operational states and anomalies, as well as to accurately locate pipe faults within the ship. Data were collected using real-world tests with FiFi system sensors, capturing both normal operations and simulated pipe ruptures, and were meticulously labeled to facilitate neural network training. Two neural network models were introduced: one for classifying system states (normal or anomalous) based on time-series pressure data, and another for identifying the location of anomalies related to pipe ruptures. Experimental results demonstrated the efficacy of these models in detecting and localizing pipe faults, with performance evaluated using mean squared error (MSE) across different network configurations. The successful implementation of these machine learning and deep learning algorithms highlights their potential for enhancing maritime safety and operational efficiency.