By Topic

Anomaly Detection and Diagnosis Algorithms for Discrete Symbol Sequences with Applications to Airline Safety

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms that we present are general and domain-independent, we focus on a specific problem that is critical to determining the system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of the longest common subsequence as a similarity measure, followed by detailed outlier analysis to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from the cluster center. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. In the final section of the paper, we demonstrate the effectiveness of sequenceMiner for anomaly detection on a real set of discrete-sequence data from a fleet of commercial airliners. We show that sequenceMiner discovers actionable and operationally significant safety events. We also compare our innovations with standard hidden Markov models, and show that our methods are superior.

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:39 ,  Issue: 1 )