Enabling Smart Retrofitting and Performance Anomaly Detection for a Sensorized Vessel: A Maritime Industry Experience

The integration of sensorized vessels, enabling real-time data collection and machine learning-driven data analysis marks a pivotal advancement in the maritime industry. This transformative technology not only can enhance safety, efficiency, and sustainability but also usher in a new era of cost-effective and smart maritime transportation in our increasingly interconnected world. This study presents a deep learning-driven anomaly detection system augmented with interpretable machine learning models for identifying performance anomalies in an industrial sensorized vessel, called TUCANA. We Leverage a human-in-the-loop unsupervised process that involves utilizing standard and Long Short-Term Memory (LSTM) autoencoders augmented with interpretable surrogate models, i.e., random forest and decision tree, to add transparency and interpretability to the results provided by the deep learning models. The interpretable models also enable automated rule generation for translating the inference into human-readable rules. Additionally, the process also includes providing a projection of the results using t-distributed stochastic neighbor embedding (t-SNE), which helps with a better understanding of the structure and relationships within the data and assessment of the identified anomalies. We empirically evaluate the system using real data acquired from the vessel TUCANA and the results involve achieving over 80% precision and 90% recall with the LSTM model used in the process. The interpretable models also provide logical rules aligned with expert thinking, and the t-SNE-based projection enhances interpretability. Our system demonstrates that the proposed approach can be used effectively in real-world scenarios, offering transparency and precision in performance anomaly detection.