With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.

Marine engines confront challenges of varying working conditions and intricate failures. Existing studies have primarily concentrated on fault diagnosis in a single condition, overlooking the adaptability of these methods in diverse working condition. To address the aforementioned issues, we propose a cross working condition fault diagnosis method named the Balanced Adaptation Domain Weighted Adversarial Network (BADWAN). This method combines transfer learning to tackle the challenges of cross working condition diagnosis with limited labels. Specifically tailored for scenarios with incomplete labeling in the target working conditions, we designed an Enhanced Centroid Balance scheme to balance the label space, thereby enhancing the model’s transfer capabilities. Additionally, we designed an Instance Affinity Weighting scheme on the foundation of Class-level Weighting, refining the model to the instance level for effective information interaction. Furthermore, we incorporated the Adaptive Uncertainty Suppression strategy to further boost the model’s classification prowess. Two experimental scenarios were designed to demonstrate the effectiveness of the proposed model using a Wärtsilä9L34DF dual-fuel engine as an experimental subject. The results demonstrate an over 90% diagnostic accuracy in scenarios with complete target working condition labels and 86% accuracy in scenarios with incomplete labels, outperforming other transfer learning models. The BADWAN model excels in cross-condition fault diagnosis tasks for marine engines with incomplete target working condition labels, offering a novel solution to this field.

At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

Deep network fault diagnosis requires a lot of labeled data and assumes identical data distributions for training and testing. In industry, varying equipment conditions lead to different data distributions, making it challenging to maintain consistent fault diagnosis performance across conditions. To this end, this paper designs a transfer learning model named the multi-adversarial joint distribution adaptation network (MAJDAN) to achieve effective fault diagnosis across operating conditions. MAJDAN uses a one-dimensional lightweight convolutional neural network (1DLCNN) to directly extract features from the original bearing vibration signal. Combining the distance-based domain-adaptive method, maximum mean difference (MMD), with the multi-adversarial network will simultaneously reduce the conditional and marginal distribution differences between the domains. As a result, MAJDAN can efficiently acquire domain-invariant feature information, addressing the challenge of cross-domain bearing fault diagnosis. The effectiveness of the model was verified based on two sets of different bearing vibration signals, and one-to-one and one-to-many working condition migration task experiments were carried out. Simultaneously, various levels of noise were introduced to the signal to enable analysis and comparison. The findings demonstrate that the suggested approach achieves exceptional diagnostic accuracy and exhibits robustness.

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.

Name Entity Recognition (NER) aims to recognize entities in the engine room domain from unstructured engine room domain text. But in the engine room domain, the entities are diverse and complex, and there is a nesting phenomenon, resulting in a low entity recognition rate. In this paper, a deep learning method incorporating language models is proposed to enhance the entity recognition performance within the engine room. domain. Firstly, the Bidirectional Encoder Representation from Transformers (BERT) language model is employed to train text feature extraction, acquiring a matrix of vector representations at the word level. Secondly, the trained word vectors are fed into the Bidirectional Gated Recurrent Unit (BiGRU) for contextual semantic entity feature extraction. Finally, the global optimal sequence is extracted by combining with the Conditional Random Field (CRF) model to obtain the named entities in the ship cabin semantics. The experimental results show that the proposed algorithm can obtain better F1 values for all three types of entity recognition. Compared with BERT-BiGRU, the overall accuracy of entity identification, recall rate and F1 value are improved by 1.35%, 1.45% and 1.40%, respectively.