Graph-Based Adaptive Anomaly Detection Framework for Dual-Fuel Marine Engines

Abstract

Dual‑fuel (DF) marine engines, capable of operating on both diesel and LNG, face significant monitoring challenges due to frequent mode switching, dual valve timing, and load variability, which create nonlinear, time‑varying dependencies among sensors. Such dynamics undermine conventional time‑series anomaly detection methods that overlook structural relationships. To address this, we propose a graph‑based anomaly detection framework tailored for DF engine monitoring. Sensor readings are modeled as nodes, with edges encoding domain‑informed physical or functional dependencies. A multi‑head Graph Attention Network (GAT)–based overcomplete autoencoder captures both local sensor behavior and global structural patterns; the expanded latent space preserves fine‑grained features and heightens sensitivity to subtle deviations. The encoder aggregates context‑aware features, and the decoder ensures graph‑consistent reconstruction. Anomalies are scored using a λ‑weighted combination of node‑level reconstruction error (RMSE) and graph‑level structural inconsistency from Graph Laplacian Smoothness (GLS). The λ parameter is optimized post hoc on validation data via F1‑score, balancing sensitivity and precision. Evaluation on ten months of DF engine data demonstrates interpretable, real‑time fault detection and sensor‑level localization, supporting practical, condition‑based maintenance.