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Introduction

Within the health-monitoring frame, fault diagnosis includes the following steps: modelling, detection, isolation andestimation. Quantitative-based methods have been successfully used so far in diverse applications. However when dealing withgradual fault and particularly in noisy environment the diagnosis becomes more challenging to obtain good performancesmeaning low false alarm and low miss detection rates. Recent results have shown that data-driven methods based on statisticalfeatures in the time, frequency, time-frequency or time-scale domains are effective for the monitoring of incipient faults (highSignal to Noise Ratio and low Fault to Noise Ratio).

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Submission Topics

Topics of the Session:

  • Data-driven approaches (mono or multi-dimensional),

  • Fault modelling, detection, estimation

  • Statisticalfeature extraction, distance measures,

  • Parametrical and non-parametrical methods,

  • Signal processing techniques (mono and multivariate),

  • Classification, discrimination

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Important Date
  • Conference Date

    Nov 05

    2017

    to

    Nov 08

    2017

  • Nov 08 2017

    Registration deadline

Sponsored By
IEEE工业电子学会
Organized By
Southeast University
Institute of Automation, Chinese Academy of Sciences
School of Mathematics and Systems Science, Chinese Academy of Sciences