The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.
To address the issues of information imbalance and the difficulty in synergistically extracting global and local features during extracorporeal membrane oxygenation (ECMO) fault diagnosis, this paper proposes a model integrating Chebyshev graph convolutional neural networks (ChebyNet) with convolutional neural networks (CNN) (CNN-ChebyNet). This model is applied to ECMO fault diagnosis tasks to efficiently enhance feature extraction accuracy. Firstly, a graph symmetry processing mechanism is introduced into the ChebyNet framework to improve the balance of information flow between nodes. Secondly, by combining ChebyNet’s global modeling capability with CNN’s local temporal feature extraction ability, multidimensional representation of complex fault features is achieved. Finally, through multi-task learning node reconstruction and classification tasks, the perception of latent correlations among samples is enhanced. Experiments on the ECMO blood pump impeller assembly dataset demonstrate that the CNN-ChebyNet model achieves superior performance across multiple comparison methods, with an average diagnostic accuracy exceeding 99%, showcasing outstanding diagnostic capability and stability. Furthermore, ablation experiments validate the effectiveness of each model component in multi-fault identification. In summary, this study provides an effective and feasible technical solution for fault diagnosis in ECMO devices.
