Research on fault diagnosis of patient monitor based on text mining

传统监护仪故障诊断多依赖人工经验，诊断效率较低且故障维修文本数据未得到有效利用。针对以上问题，本文提出一种基于多特征文本表示以及改进的双向门控循环神经元网络（BiGRU）和注意力机制的监护仪故障智能诊断方法。首先，对文本进行预处理，采用基于转换器（Transformer）的语言激励双向编码器表示生成含有多种语言学特征的词向量；然后，通过改进的BiGRU和注意力机制对双向故障特征分别进行提取并加权；最后，使用加权损失函数降低类别不平衡对模型的影响。为证实所提方法的有效性，本文使用监护仪故障数据集进行验证，总体宏 F1值达到91.11%。该结果表明，本文所建模型可实现故障文本的自动分类，或可为今后监护仪故障智能诊断提供辅助决策支持。.; 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.