Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.

具有深度学习方法的大数据分析在交通运输工程中已引起越来越多的关注，涉及操作，维护和安全。在商用航空领域，现代飞机上产生的运行和维护数据呈指数级增长，对这些数据的预测分析是航空维护中令人兴奋且充满希望的领域，这可能会改变航空航天维护行业。这项研究说明了深度学习在大数据分析中用于预测性维护的最新应用，以及商用飞机的实际案例研究。提出了一种基于长期短期记忆网络的自动编码器（LSTM-AE），用于复杂飞机系统的故障检测和分类，该方法利用了来自异构传感器的原始时间序列数据。所提出的方法使用对应于系统健康行为的标称时间序列样本来学习基于LSTM-AE框架的重建模型。然后，从重构误差矩阵中导出系统健康指数（HI）和故障特征向量，以进行故障检测和分类。在商用飞机机队的真实数据集上演示了该方法。基于提取的特征，可以成功检测并区分出典型的PCV故障以及由于感应管线漏气而导致的390 F传感器故障和450 F传感器故障。案例研究结果表明，所计算的HI可以有效地描述飞机系统的健康状态，并且可以高置信度地识别出不同的故障类型，这有助于线路故障排除。