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This study presents an industry experience showcasing a vessel operationalanomaly detection approach that utilizes semi-supervised deep learning modelsaugmented with lightweight interpretable surrogate models, applied to anindustrial sensorized vessel, called TUCANA. We leverage standard and LongShort-Term Memory (LSTM) autoencoders trained on normal operational data andtested with real anomaly-revealing data. We then provide a projection of theinference results on a lower-dimension data map generated by t-distributedstochastic neighbor embedding (t-SNE), which serves as an unsupervised baselineand shows the distribution of the identified anomalies. We also developlightweight surrogate models using random forest and decision tree to promotetransparency and interpretability for the inference results of the deeplearning models and assist the engineer with an agile assessment of the flaggedanomalies. The approach is empirically evaluated using real data from TUCANA.The empirical results show higher performance of the LSTM autoencoder -- as theanomaly detection module with effective capturing of temporal dependencies inthe data -- and demonstrate the practicality of the lightweight surrogatemodels in providing helpful interpretability, which leads to higher efficiencyfor the engineer's decision-making.

A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models