An incremental clustering method for anomaly detection in flight data. (November 2021)

• Record Type: Journal Article
• Authors: Zhao, Weizun; Li, Lishuai; Alam, Sameer; Wang, Yanjun
• Is Part Of: Transportation research. Volume 132(2021)
• Publication Date: 2021-11
• Subjects: Gaussian mixture model -- Incremental clustering -- Flight data -- Anomaly detection; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103406
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics. Highlights: An incremental cluster method to identify patterns and outliers in flight data. The method uses robust Gaussian Mixture Model to initialize clusters offline. The method incrementally updates its clusters as new data come in online. The method was tested on simulation data and real-world flight data. Results showed that the method could handle dynamically growing data well.