为了解决工业生产设备故障领域的问答系统缺乏标注数据、意图识别槽位填充性能不足的问题，提出了一种基于Transformer的多层双向自注意编码器（bidirectional encoder representations from transformers, BERT）的联合模型。利用BERT进行文本序列编码，并通过双向长短时记忆网络（bidirectional long short-term memory, Bi-LSTM）捕捉文本上下文语义关系。通过最大池化和致密层提取关键信息，同时使用条件随机场（conditional random field, CRF）增强模型泛化能力。构建了工业领域设备故障问答语料库，并提出了针对该领域的模型部署框架。在ATIS等公共数据集上进行实验，相对于基线模型，本文模型在句子级准确率、F₁和意图识别准确率上，分别提高4.4、2.1和0.5个百分点。研究结果有效提升了问答系统性能，为缺乏工业生产数据的问答系统领域提供了数据集和部署框架。

To address the lack of annotated data and insufficient performance in intent detection and slot filling in the domain of industrial equipment failure, a joint model based on BERT was proposed. BERT was utilized for text sequence encoding, while a bidirectional long short-term memory (Bi-LSTM) network was employed to capture the semantic relationships within the context. Max pooling and dense layers were used to extract key information, and a conditional random field (CRF) was incorporated to enhance the model's generalization capability. A question-and-answer corpus specifically tailored to the industrial domain of equipment failure was constructed, and a deployment framework for this domain was proposed. Experimental evaluations conducted on public datasets such as ATIS demonstrated that the proposed model outperforms baseline models by improving sentence-level accuracy, F₁ score, and intent detection accuracy by 4.4%, 2.1%, and 0.5% respectively. This research effectively enhances the performance of question-and-answer systems and provides a dataset and deployment framework for the field of industrial equipment failure, which lacks sufficient real-world data.