A Hierarchical Fault Diagnosis Method For Marine Engine Using Isolation Forest and Extreme Learning Machine with PADBSCAN

In the age of digitalization and automation, research on data-driven marine engine fault diagnosis is ongoing. However, the volume of normal data in real-world environments significantly exceeds that of fault data, and there will be unknown fault modes that are not collected, which leads to incorrect diagnosis. To address the challenge of open-set fault diagnosis for marine engines under conditions of data imbalance, this paper proposes a hierarchical fault diagnosis method, based on Isolation Forest (iForest), Extreme Learning Machine (ELM) and Parameter-Adaptive Density-Based Spatial Clustering of Applications with Noise (PADBSCAN), named HFD-IEP. To validate the effectiveness of the proposed method, AVL BOOST was utilized to simulate a two-stroke marine diesel engine. The simulation results confirm the method's effectiveness.