Novelty detection: a review—part 1: statistical approaches
Signal Processing
Unknown word sense detection as outlier detection
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Anomalistic sequence detection
International Journal of Intelligent Information and Database Systems
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
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The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stress event of a particular severity. These matrices are used to determine how much of the aircraft's life has been used up in each flight. Unfortunately, the sensors that produce this data are subject to degradation themselves, resulting in corruption of FOOMs. This paper reports a method of automating detection of sensor faults. It is the only known method that is capable of detecting such faults. The method is in essence a dimensionality reduction algorithm coupled to a novelty detection algorithm that produce measures of unusual counts of stress events at the level of the individual cell and unusual distributions of counts over the entire FOOM. Cell-level error is detected using a probability threshold and a sum of standard deviations. FOOM-level error is detected using a novel application of the Eigen-face algorithm. Novelty is measured using Gaussian basis function neural network fitted using the Expectation-Maximization algorithm.