The whole train design of an Electric Multiple Unit (EMU) involves multiple domains and scenarios, thus requiring comprehensive consideration of various factors during the design process. Traditional design methods often utilize text-based approaches to model systems; however, such documentation-based designs often suffer from semantic heterogeneity, inconsistent data sources, and also struggle to provide a more intuitive overview of the overall design process. To address these issues, this paper proposes a method based on ontology–knowledge collaborative drive to achieve integration of the overall EMU design. Firstly, we employ the System Modeling Language (SysML) to construct the Model-Based Systems Engineering (MBSE) model of the EMU, establishing functional and physical architecture element models, with the EMU MBSE model serving as input. Subsequently, in the requirement model, architecture model, and traceability model, we utilize top-level ontology to construct the EMU ontology framework in a top-down manner. Lastly, leveraging the Neo4j database, we employ a knowledge graph (KG) approach to fill domain knowledge into each model in a bottom-up manner, thereby realizing the ontology–knowledge collaborative drive for the overall EMU design construction. The effectiveness of the proposed method is validated using the EMU Passenger Information System (PIS) and Traction transformer System (TS) as examples.

On-board system fault knowledge base (KB) is a collection of fault causes, maintenance methods, and interrelationships among on-board modules and components of high-speed railways, which plays a crucial role in knowledge-driven dynamic operation and maintenance (O&M) decisions for on-board systems. To solve the problem of multi-source heterogeneity of on-board system O&M data, an entity matching (EM) approach using the BERT model and semi-supervised incremental learning is proposed. The heterogeneous knowledge fusion task is formulated as a pairwise binary classification task of entities in the knowledge units. Firstly, the deep semantic features of fault knowledge units are obtained by BERT. We also investigate the effectiveness of knowledge unit features extracted from different hidden layers of the model on heterogeneous knowledge fusion during model fine-tuning. To further improve the utilization of unlabeled test samples, a semi-supervised incremental learning strategy based on pseudo labels is devised. By selecting entity pairs with high confidence to generate pseudo labels, the label sample set is expanded to realize incremental learning and enhance the knowledge fusion ability of the model. Furthermore, the model’s robustness is strengthened by embedding-based adversarial training in the fine-tuning stage. Based on the on-board system’s O&M data, this paper constructs the fault KB and compares the model with other solutions developed for related matching tasks, which verifies the effectiveness of this model in the heterogeneous knowledge fusion task of the on-board system.