A Domain-Guided Feature-Fusion Framework for Ship Equipment Based on Multi-Type Features

Reliability assessment of ship equipment is often constrained by insufficient or unavailable failure data, particularly for highly reliable components with extremely low failure frequencies. To support the use of reference information from similar equipment in subsequent reliability analysis, this study proposes a domain-guided multi-type feature-fusion framework for ship equipment clustering. The proposed framework addresses the heterogeneous nature of ship equipment records by integrating textual, categorical, and numerical attributes into a unified representation. Specifically, equipment names and specification/model information are represented using character-level TF-IDF features; categorical attributes are encoded through One-Hot representation, and numerical attributes are processed using logarithmic transformation and standardization. Six engineering attributes, including equipment name, specification/model information, technical category, measurement unit, number of installations per platform, and reference unit price, are incorporated into the fused feature space. In addition, a domain-knowledge-driven feature-group weighting mechanism is introduced to emphasize attributes that directly reflect functional and technical similarities, especially equipment names and specification/model information. K-Means clustering is then performed in the weighted fused feature space, and the number of clusters is determined by jointly considering the Silhouette Coefficient, Calinski–Harabasz index, Davies–Bouldin index, and cluster-size distribution. Experiments on 1345 practical ship equipment samples show that K = 36 provides a reasonable balance among clustering structure, candidate-set availability, engineering consistency, and stability under random initialization. Compared with the equal-weight scheme, Attribute-Weighted K-Means, K-Prototypes, and Gower-based hierarchical clustering, the proposed framework achieves higher equipment-name and specification/model similarities while maintaining meaningful technical-category consistency. These results indicate that the proposed framework can effectively identify latent functional and technical similarity relationships among ship equipment and provide candidate reference sets for reliability assessment under sparse failure data conditions.