关键词: 联合收割机, 故障领域, 命名实体识别, 知识图谱, 预训练模型, 对抗样本

Abstract:  Combine harvesters as a kind of mechanized equipment will inevitably have mechanical failure, in order to quickly find out the relevant fault entity and solve the mechanical failure, a named entity recognition model RP-TEBC (RoBERTa-wwm-ext+PGD+Transformer-Encoder+BiGRU+CRF) for combine harvester fault field is proposed. RP-TEBC uses the dynamically encoded RoBERTa-wwm-ext pre-trained model as the word embedding layer, uses the adaptive Transformer encoder layer to fuse the Bidirectional Gating Unit (BiGRU) as the context encoder, and finally uses the conditional random field (CRF) as the decoder layer, using the Viterbi algorithm to find the optimal path output. At the same time, the RP-TEBC model generates adversarial samples by adding some perturbations in the word embedding layer. Through continuous training and optimization of the model, the overall robustness and generalization performance of the model can be improved. On the constructed named entity recognition data set in the field of combine harvester faults, experiments have shown that compared with the baseline model, the accuracy, recall rate, and F1 value of this model have increased by 1.79%, 1.01%, and 1.46% respectively.

Key words: combine harvester, fault domain, named entity recognition, knowledge graph, pre?trained model, adversarial sample