摘要:在飞机维修与保养过程中，航空维修公司已积累了大量经验性的维修日志数据.合理利用该类维修日志，结合机器学习方法，可以辅助维修人员做出正确的故障诊断决策.首先，针对维修日志的特殊性，提出一种迭代式的故障诊断基本过程；其次，在传统的文本特征提取技术的基础上，基于领域内信息，提出一种基于卷积神经网络（convolution neural network，简称CNN）的小样本文本特征提取方法，在样本量较少的情况下，利用预测目标将字向量作为输入，得到更为充分的文本特征；最后，使用随机森林（random forest，简称RF）模型，结合其他故障特征判别飞机设备的故障原因.卷积神经网络以故障原因为目标，预先对故障现象中的字向量进行训练，从而得到更能反映该领域的文本特征.与其他文本特征提取方法相比，该类方法在小样本数据上得到了更好的效果.同时，将卷积神经网络与随机森林模型应用于飞机设备的故障原因判别，并与其他文本特征提取方式和机器学习预测模型进行对比，说明了该类文本特征提取方式和故障原因判别方法的合理性和必要性.



Abstract:In the process of aircraft maintenance, the aviation maintenance company has accumulated a large number of empirical maintenance log data. Machine learning methods can be used to help maintenance staff to make correct fault diagnosis decisions, using this type of maintenance log reasonably. Firstly, according to the particularity of the maintenance log, an iterative fault diagnosis process is proposed. Secondly, based on the traditional text feature extraction technology, the text feature extraction method based on convolution neural network (CNN) with the information in the domain is proposed, which is used in the case of small sample size. The method uses the target vector to train word vector to get more adequate text features. Finally, the random forest (RF) model is used in combination with other fault characteristics to determine the cause of aircraft equipment failure. The convolutional neural network aims at the cause of the failure, and pre-trains the word vector in the fault phenomenon to obtain a text feature that better reflects the field. Compared with other text feature extraction methods, the method obtains better results in the case of small sample size. At the same time, the convolutional neural network and random forest model are applied to the identification of aircraft equipment failure, and compared with other text feature extraction methods and machine learning prediction models, which illustrates the rationality and necessity of the method of text feature extraction and the method of fault cause identification.



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