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In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fault diagnosis model is proposed in this paper based on Word to Vector (Word2vec) and prior-knowledge Convolutional Neural Network (CNN). The fault text first enters Word2vec to perform text feature extraction, and the extracted text feature vectors are then input into the proposed prior-knowledge CNN to train the fault classifier. The prior-knowledge CNN introduces expert fault knowledge through Cloud Similarity Measurement (CSM) to improve the performance of the fault classifier. Validation experiments on five-year maintenance log data of a civil aircraft were carried out to successfully verify the effectiveness of the proposed model.

Water is the source of life, and in recent years, with the progress in technology, water quality data have shown explosive growth; how to use the massive amounts of data for water quality prediction services has become a new opportunity and challenge. In this paper, we use the surface water quality data of an area in Beijing collected and compiled by Zhongguancun International Medical Laboratory Certification Co., Ltd. (Beijing, China). On this basis, we decompose the original water quality indicator data series into two series in terms of trend and fluctuation; for the characteristics of the decomposed series data, we use the traditional time series prediction method to model the trend term, introduce the deep learning method to interpret the fluctuation term, and fuse the final prediction results. Compared with other models, our proposed integrated Wavelet decomposition, Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU) model, which is abbreviated as the W-ARIMA-GRU model, has better prediction accuracy, stability, and robustness for three conventional water quality indicators. At the same time, this paper uses the ensemble learning model LightGBM for the prediction of water quality evaluation level, and the accuracy and F1-score reached 97.5% and 97.8%, respectively, showing very strong performance. This paper establishes a set of effective water quality prediction frameworks that can be used for timely water quality prediction and to provide a theoretical model and scientific and reasonable analysis reference for the relevant departments for advanced control.

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