Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems
This addresses a practical challenge for the shipping industry by enabling fault diagnosis methods to handle previously unseen faults, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of open-set fault diagnosis in marine machinery systems, where unknown fault types can occur during testing, and proposes a semi-supervised framework that achieves effective classification of known faults and detection of unknown samples, as demonstrated on a public maritime benchmark dataset.
Recently, fault diagnosis methods for marine machinery systems based on deep learning models have attracted considerable attention in the shipping industry. Most existing studies assume fault classes are consistent and known between the training and test datasets, and these methods perform well under controlled environment. In practice, however, previously unseen or unknown fault types (i.e., out-of-distribution or open-set observations not present during training) can occur, causing such methods to fail and posing a significant challenge to their widespread industrial deployment. To address this challenge, this paper proposes a semi-supervised open-set fault diagnosis (SOFD) framework that enhances and extends the applicability of deep learning models in open-set fault diagnosis scenarios. The framework includes a reliability subset construction process, which uses a multi-layer fusion feature representation extracted by a supervised feature learning model to select an unlabeled test subset. The labeled training set and pseudo-labeled test subset are then fed into a semi-supervised diagnosis model to learn discriminative features for each class, enabling accurate classification of known faults and effective detection of unknown samples. Experimental results on a public maritime benchmark dataset demonstrate the effectiveness and superiority of the proposed SOFD framework.