Anomaly detection in aviation data using extreme learning machines

We develop fast anomaly detection algorithms using extreme learning machines (ELM) to discover operationally significant anomalies in large aviation data sets. Anomaly detection (aka one-class classification or outlier detection) is an active area of research to identify safety risks in aviation. Aviation data is characterized by high dimensionality, heterogeneity (continuous and categorical variables), multimodality and temporality. To address these challenges, NASA Ames has developed several anomaly detection algorithms including MKAD, the present state of the art [1]. MKAD's computational complexity is quadratic with respect to the number of training examples which makes it time consuming (and sometimes infeasible) for mining very large data sets. In this paper, we utilize ELM's fast training and good generalization properties to develop scalable anomaly detection algorithms for very large data sets. We adapt unsupervised ELM algorithms such as the autoencoder and embedding models to perform anomaly detection. The unsupervised models capture the nominal data distribution and by choosing a desired strength of detection that defines the upper bound of outliers in the training data, the anomaly decision boundary is determined. The autoencoder model detects anomalies as the ones that have a large reconstruction error while the embedding model detects anomalies as the ones that lie outside a hypersphere in the embedded space. The proposed algorithms are applied to a real aviation safety benchmark problem and the results show that the ELM based algorithms are comparable to MKAD in detection while training is made faster by two orders of magnitude.