Standard Article

Novelty Detection

Signal Processing

  1. Lionel Tarassenko1,
  2. David A. Clifton1,2,
  3. Peter R. Bannister1,
  4. Steve King3,
  5. Dennis King3

Published Online: 15 SEP 2009

DOI: 10.1002/9780470061626.shm183

Encyclopedia of Structural Health Monitoring

Encyclopedia of Structural Health Monitoring

How to Cite

Tarassenko, L., Clifton, D. A., Bannister, P. R., King, S. and King, D. 2009. Novelty Detection. Encyclopedia of Structural Health Monitoring. .

Author Information

  1. 1

    University of Oxford, Department of Engineering Science, Oxford, UK

  2. 2

    Oxford BioSignals Ltd, Abingdon, UK

  3. 3

    Rolls-Royce Civil Aero-Engines, Derby, UK

Publication History

  1. Published Online: 15 SEP 2009


Novelty detection is described here as a data-driven technique in which a probabilistic model of normality is constructed from (normal) training data, so that subsequent departures from expected behavior can be identified as novel events. The first step in constructing the model of normality is to select features that characterize normal behavior, but which are also likely to change during periods of abnormal behavior. Data visualization techniques, such as the NeuroScale dimensionality-reduction mapping, are helpful in investigating the distribution of the features over the space of normal data, especially at the boundaries of this space. We show, in this article, how the Parzen windows density estimator can be used to characterize normal vibration behavior and identify an unexpected event during the development phase of a three-shaft jet engine. We discuss how the novelty threshold may be set in principled fashion using extreme value statistics and present results for two types of vibration feature vectors; one is based on the real-time measurement of vibration levels at harmonically related frequencies and the other, a speed-based vibration signature, summarizing the entire flight.


  • novelty detection;
  • engine health monitoring;
  • vibration;
  • data visualization;
  • probabilistic models of normality;
  • extreme value statistics