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In order to reduce error accumulation caused by multistep modeling and achieve general accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as model input and multistep RUL values as model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into multi-head attention BiGRU. Aeroengine and rolling bearing are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%-70%.

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Aviation unsafe events often lead to major casualties and property losses. Aviation safety risk intelligent early warning is an important means to ensure the safe and reliable operation of aircraft. Therefore, an intelligent early warning model is urgently needed to quickly predict the risk level and identify potential risks to take targeted measures to realize the active management of safety. To realize the above process, the text mining method is used to extract key risk information from unsafe event reports and input it into the intelligent early warning model to predict its risk level, further constructing the priority processing index to achieve a rapid decision, and finally realize the intelligent safety management process of features extraction to early warning levels identification and then to priority processing. First, domain dictionary and Chinese stop word list are constructed to process the massive text data in the unsafe event’s report. Further, TF-IDF and TextRank are fused to extract key risk information and convert it into feature vectors. Second, the IHT algorithm is used to alleviate the sample class imbalance problem. After that, input the balanced risk information into an improved stacking multi-model fusion algorithm to accurately identify the early warning level and improve the level of active management and control via priority processing index ranking. The effectiveness and feasibility of the proposed method are demonstrated by testing the unsafe event text data of some aircraft maintenance companies and airlines, which promotes the practical application value of text mining technology in the aviation field.