A novel feature selection algorithm for high-dimensional condition monitoring data

Kui Zhang, Andrew Ball, Yuhua Li, Fengshou Gu

    Research output: Contribution to journalArticle

    Abstract

    The technique of machinery condition monitoring has been greatly enhanced over recent years with the application of many effective classifiers. However, these classification methods suffer from the ‘curse of dimensionality’ when applied to high-dimensional condition monitoring data. Actually, many classification algorithms are simply intractable when the number of features in the data is sufficiently large. In order to solve the problem, engineers have to resort to complicated feature extraction methods and other statistical theories to reduce the data dimensionality. However, features extracted using these methods lose their original engineering meaning and become obscure for engineers. In this study, a novel feature selection algorithm is presented to help to identify machinery condition quickly, based only on frequency spectrum data and without considering any complicated feature extraction methods. This brings many significant benefits: it can not only help engineers out of difficulties with complicated feature extraction methods, but it also gives a clear insight into the real condition of machinery without any loss of engineering meaning from the features used. The algorithm forms a functional extension of a relevance vector machine to feature selection, with a fast step-variable sequential backward search algorithm to search relevant scale parameters within a kernel function. Two case studies on different levels of condition monitoring are conducted to demonstrate the potential of applying the algorithm to high-dimensional engineering data.
    Original languageEnglish
    Pages (from-to)33-43
    JournalInternational Journal of Condition Monitoring
    Volume1
    Issue number1
    Publication statusPublished - 1 Jun 2011

    Fingerprint Dive into the research topics of 'A novel feature selection algorithm for high-dimensional condition monitoring data'. Together they form a unique fingerprint.

  • Cite this