Two-stage classifications for improving time-to-failure estimates: a case study in prognostic of train wheels

Authors:
Chunsheng Yang;Sylvain Létourneau
Affiliations:
National Research Council Canada, Ottawa, Canada K1A 0R6;National Research Council Canada, Ottawa, Canada K1A 0R6
Venue:
Applied Intelligence
Year:
2009

Citing 11
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Abstract

In order to meet the need for higher equipment availability and lower maintenance cost, much attention is being paid to the development of prognostic systems. Such systems support a proactive maintenance strategy by continuously monitoring the components of interest and predicting their failures sufficiently in advance to avoid disruptions during operation. Recent research demonstrated the potential of a comprehensive data mining methodology for building prognostic models from readily available operational and maintenance data. This approach builds a binary classifier that can determine the likelihood of a failure within a broad target window but cannot provide precise time to failure (TTF) estimations. This paper introduces a two-stage classification approach that helps improve the precision of TTF estimations. The new approach uses the initial methodology to learn a variety of base classifiers and then relies on meta-learning to integrate them. The paper details the model building process and demonstrates the usefulness of the proposed approach through a real-world prognostic application.