Instance-Based Learning Algorithms
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Spectral analysis of irregularly-sampled data: Paralleling the regularly-sampled data approaches
Digital Signal Processing
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In this paper we consider the problem of detecting anomalies in sample series obtained from critical train subsystems. Our study is the analysis of charging pressure in turbodiesel engines powering a fleet of passenger trains. We describe an automated methodology for (i) labelling time series samples as normal, abnormal or noisy, (ii) training supervised classifiers with labeled historical data, and (iii) combining classifiers to filter new data. We provide experimental evidence that our methodology yields error rates comparable to those of an equivalent manual process.