关键词: 异常检测, 接地点远事件(LTE), 自编码器, 隐马尔可夫模型(HMM), 快速存取记录器(QAR)

Abstract: The existing flight operation quality assurance (FOQA) standard only uses the integral distance of the ground speed to define the long touchdown exceedance (LTE), which cannot detect and explain the exceedance using multiple quick access recorder (QAR) parameters. The QAR samples with multiple parameters were segmented by the sliding window, and the segmented sample sets were generated according to the segmentation position. The representation of the QAR sample segmentation and the vector within each QAR segment was obtained by the long short-term memory (LSTM) networks autoencoder, and the representation vectors were clustered by K-means to realize the symbolization of the QAR samples and the QAR segments. The hidden Markov model (HMM) model was built by using the symbolic sequence of the QAR sample set of the normal flights, which was used to detect the flights with the LTE. The second HMM model was constructed from the symbolic sequences of the segment of the normal QAR samples and the QAR samples including the LTE. Then the Viterbi algorithm was used to determine the specific positon of the LTE in the QAR sample segment. Experimental results on real QAR data sets show that, compared with other multi-dimensional time series anomaly detection methods, the proposed method can not only effectively detect the LTE, but also obtain the outliers of multiple QAR parameters, which can assist domain experts to analyze the cause of the exceedance.

Key words: anomaly detection, long touchdown exceedance (LTE), autoencoder, hidden Markov model (HMM), quick access recorder (QAR)