Marine permanent magnet synchronous propulsion motors have problems, such as low reliability and difficult maintenance in the traditional control. In this paper, a sensorless control system for a permanent magnet synchronous motor (PMSM) based on parameter identification is proposed. According to the mathematical model of the motor in the two-phase synchronous rotating coordinate system, a model reference adaptation system (MRAS) is used to estimate the rotor speed and rotor position of the motor. Because the MRAS is highly dependent on the motor parameters, and they will change with the environment, working state, etc., the Adaline neural network is used to identify the motor parameters online, and then the model parameters in the MRAS are corrected. The simulation results show that the combined control system can reduce the estimated error of the rotor speed by about 50% compared with the traditional method, and reduces the rotor position angle estimation error by 96%. It shows that the combined system can accurately estimate the rotational speed and rotor position of the motor, and it has high identification accuracy for the motor parameters.

With the development of intelligentization in maritime vessels, the pursuit of an organized and scalable knowledge storage approach for marine engine room systems has become one of the current research hotspots. This study addressed the foundational named entity recognition (NER) task in constructing a knowledge graph for marine engine rooms. It proposed an entity recognition algorithm for Chinese semantics in marine engine rooms that integrates language models. Firstly, the bidirectional encoder representation from transformers (BERT) language model is used to extract text features and obtain word-level granularity vector matrices. Secondly, the trained word embeddings are fed into a bidirectional long short-term memory network (BiLSTM) to extract contextual information. It considers the surrounding words and their sequential relationships, enabling a better understanding of the context. Additionally, the conditional random field (CRF) model was used to extract the globally optimal sequence of named entities in the marine engine room semantic. The CRF model considered the dependencies between adjacent entities that ensured a coherent and consistent final result for entity recognition in marine engine room semantics. The experiment results demonstrate that the proposed algorithm achieves superior F1 scores for all three entity types. Compared with BERT, the overall precision, recall, and F1 score of the entity recognition are improved by 1.36%, 1.41%, and 1.38%, respectively. Future research will be carried out on named entity recognition of a small sample set to provide basic support for more efficient entity relationship extraction and construction of a marine engine room knowledge graph.