A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

Traditional aircraft maintenance support work is mainly based on structured data. Unstructured data, such as text data, have not been fully used, which means there is a waste of resources. These unstructured data contain a great storehouse of fault knowledge, which could provide decision support for aircraft maintenance support work. Therefore, a text-based fault diagnosis model is proposed in this paper. The proposed method uses Word2vec to map text words into vector space, and the extracted text feature vectors are then input into the classifier based on a stacking ensemble learning scheme. Its performance has been validated using a real aircraft fault text dataset. The results show that the fault diagnosis accuracy of the proposed method is 97.35%, which is about 2% higher than that of the suboptimal method.