Statistical Anomaly Detection for Train Fleets

practical applications every day. It has been used for fraud detection and intrusion detection for a long time, but in later years the usage has exploded to all kind of domains, like surveillance, industrial system monitoring, epidemiology, and so on. For an overview of different anomaly-detection methods and applications, see, for example, Chandola, Banerjee, and Kumar (2009). The approach taken in statistical anomaly detection is to use data from (predominantly normal) previous situations to build a statistical model of what is normal. New situations are compared against that model and are considered anomalous if they are too improbable to occur in that model. Various statistical anomaly-detection methods have previously been applied to a wide variety of problems including intrusion detection (GarcíaTeodoro et al. 2009; Cemerlic, Yang, and Kizza 2008; Chebrolu, Abraham, and Thomas 2005; Puttini, Marrakchi, and Mé 2003), fault detection and diagnosis (Lerner et al. 2000), spam filtering (Su and Xu 2009), environmental anomaly detection (Hill, Minsker, and Amir 2009), and energy expenditure estimation (Shahabdeen, Baxi, and Nachman 2010). The Swedish Institute of Computer Science (SICS) has for several years developed methods for statistical anomaly detection based on a framework called Bayesian principal anomaly (Holst and Ekman 2011). The framework has already been successfulArticles