Enabling Smart Retrofitting and Performance Anomaly Detection for a
  Sensorized Vessel: A Maritime Industry Experience
Mahshid Helali Moghadam Mateusz Rzymowski Lukasz Kulas
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.