Timely and accurate fault diagnosis of ship oil purifiers is essential for maintaining the operational reliability of a degree-4 maritime autonomous surface ship (MASS). Conventional approaches rely on manual feature engineering or simple machine learning classifiers, limiting their robustness in dynamic maritime environments. This study proposes an adaptive latent space learning framework that couples a two-dimensional convolutional autoencoder (2D-CAE) with a component-aware triplet-loss regularizer. The loss term structures the latent space to reflect both the fault severity progression and component-specific distinctions, enabling severity-proportional distances among a latent vector learned directly from vibration signals even in a limited data environment. Using data collected on a dedicated ship oil purifier test bed, the method yields a latent vector that encodes the fault severity and physical provenance, enhancing the interpretability and diagnostic accuracy. Experiments demonstrate enhanced performance over state-of-the-art deep models, while offering clear insight into fault evolution and inter-component dependencies. The framework thus advances intelligent, condition-based maintenance for autonomous maritime systems.

For stable operation of autonomous surface ships, real-time anomaly detection of engine conditions must be coupled with an operational framework that sustains model performance in dynamic maritime environments. This study proposes an autonomous maintenance system that combines a subsystem-level condition-based maintenance (CBM) model with a dedicated MLOps framework. The main engine is decomposed into multiple functional component units, each governed by an independent diagnostic pipeline that applies a hybrid algorithm combining an attention LSTM autoencoder with an isolation forest to capture subtle anomalies. Although this hybrid attains higher precision than conventional single models, it remains sensitive to operating environment shifts. To address this, we develop an onboard MLOps pipeline that monitors distributional shifts in real-time sensor data and executes an autonomous maintenance mechanism, retraining and redeploying models on local data when performance degradation is anticipated. A dual-monitoring rule set based on a standardized deviation score and its smoothed change rate is used to discriminate abrupt mechanical anomalies from gradual drift. Experiments on a fault simulation testbed indicate that, under data drift, the system can recover detection reliability and adapt to changing engine conditions, providing a technical basis for the self-sustaining reliability of autonomous surface ships.