基于民机维修文本数据的故障诊断方法

Fault diagnosis method based on civil aircraft maintenance text data

在民航检修与维护过程中积累了大量蕴含丰富故障特征的文本维修记录,然而由于维修文本本身存在复杂性,其还未实现智能诊断,数据利用率低。提出一种不断修正迭代的基于预训练语言模型双向转换器编码表示(BERT)及轻量级梯度提升机(LightGBM)的飞机维修记录的故障原因分析方法,求解文本形式的维修记录中的故障原因,用以辅助维修人员进行正确的维修决策。首先,在基于BERT的故障诊断模型Transformer特征提取架构中引入多头注意力机制,以充分捕捉融合上下文的双向语义、更加关注于重点词汇;其次,为了提高诊断速度减少模型的参数并融合LightGBM模型来实现维修文本的故障原因分类;最后,将改进的模型与其他常用文本分析模型进行对比实验,在基于民机维修文本的故障诊断中该模型的准确率比TextCNN模型、LSTM模型和BiLSTM模型分别提升了38.99%、22.98%和18.16%,且BERT-LightGBM模型比BERT模型诊断速度提升了0.91%。表明所提方法在实现飞机维修文本故障诊断方面的有效性及优越性。

In the process of civil aviation repair and maintenance,a large number of text maintenance records containing rich fault features have been accumulated.However,due to complexity of the maintenance text itself,intelligent diagnosis has not been realized,and the data utilization rate is low.An aircraft fault diagnosis method based on civil aircraft maintenance text data is proposed using the Bidirectional Encoder Representation from Transformers(BERT)based on the pretrained language model and the Light Gradient Boosting Machine(LightGBM),so as to solve the fault cause in the text maintenance records and then assist maintenance personnel to make correct maintenance decisions.Firstly,the multi-head self-attention mechanism is introduced into the feature extraction architecture of the Bert-based transformer fault diagnosis model to fully capture the bi-directional semantics in the context and pay more attention to key words.Secondly,to improve the diagnosis speed,the parameters of the model are reduced,and the LightGBM model is integrated to achieve fault cause classification of maintenance texts.Finally,the improved model is compared with other commonly used text analysis models.In the fault diagnosis based on civil aircraft maintenance text,the accuracy of the model is 38.99%,22.98%and 18.16%higher than that of TextCNN model,LSTM model and BiLSTM model respectively,and the diagnostic speed of BERT LightGBM model is 0.91%higher than that of BERT model.It shows that the proposed method is effective and superior in fault diagnosis of aircraft maintenance text.