Journal article Open Access

Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

Chhaya Grover; Neelam Turk

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:contributor>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</dc:contributor>
  <dc:creator>Chhaya Grover</dc:creator>
  <dc:creator>Neelam Turk</dc:creator>
  <dc:description>Rolling element bearing health condition is monitored by analysing its vibration signature. Raw vibration signal picked up through suitably placed accelerometers is difficult to analyse hence many signal processing techniques have been proposed and developed by researchers to process the data for suitably extracting an effective signal feature set. Various machine learning techniques have been used for interpretation and accurate fault diagnosis using this extracted feature set. In this study “Empirical mode decomposition” is used for pre-processing the raw vibration data. Six “Statistical features” are extracted from the best Intrinsic mode function obtained through EMD and “Ensemble machine learning classifiers” are used for bearing fault diagnosis. A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness.</dc:description>
  <dc:source>International Journal of Engineering and Advanced Technology (IJEAT) 9(3) 350-358</dc:source>
  <dc:subject>Empirical mode decomposition, Ensemble classifiers, Statistical features, Vibration signature analysis</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Rolling Element Bearing Fault Detection using  Statistical Features and Ensemble Classifiers</dc:title>
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