Journal article Open Access

Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

Chhaya Grover; Neelam Turk


DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://zenodo.org/record/5566667">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5566667</dct:identifier>
    <foaf:page rdf:resource="https://zenodo.org/record/5566667"/>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Chhaya Grover</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Department of Electronics Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India.</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Neelam Turk</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>J. C. Bose University of Science and Technology, YMCA, Faridabad, India.</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>Empirical mode decomposition, Ensemble classifiers, Statistical features, Vibration signature analysis</dcat:keyword>
    <dct:subject>
      <skos:Concept>
        <skos:prefLabel>2249-8958</skos:prefLabel>
        <skos:inScheme>
          <skos:ConceptScheme>
            <dct:title>issn</dct:title>
          </skos:ConceptScheme>
        </skos:inScheme>
      </skos:Concept>
    </dct:subject>
    <dct:subject>
      <skos:Concept>
        <skos:prefLabel>C4836029320/2020©BEIESP</skos:prefLabel>
        <skos:inScheme>
          <skos:ConceptScheme>
            <dct:title>handle</dct:title>
          </skos:ConceptScheme>
        </skos:inScheme>
      </skos:Concept>
    </dct:subject>
    <schema:sponsor>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Blue Eyes Intelligence Engineering &amp; Sciences Publication (BEIESP)</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Publisher</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </schema:sponsor>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-02-29</dct:issued>
    <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/>
    <owl:sameAs rdf:resource="https://zenodo.org/record/5566667"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5566667</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <dct:relation rdf:resource="http://issn.org/resource/ISSN/2249-8958"/>
    <owl:sameAs rdf:resource="https://doi.org/10.35940/ijeat.C4836.029320"/>
    <dct:description>&lt;p&gt;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 &amp;ldquo;Empirical mode decomposition&amp;rdquo; is used for pre-processing the raw vibration data. Six &amp;ldquo;Statistical features&amp;rdquo; are extracted from the best Intrinsic mode function obtained through EMD and &amp;ldquo;Ensemble machine learning classifiers&amp;rdquo; 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.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL rdf:resource="https://doi.org/10.35940/ijeat.C4836.029320"/>
        <dcat:byteSize>542318</dcat:byteSize>
        <dcat:downloadURL rdf:resource="https://zenodo.org/record/5566667/files/C4836029320 (1).pdf"/>
        <dcat:mediaType>application/pdf</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
</rdf:RDF>
17
18
views
downloads
Views 17
Downloads 18
Data volume 11.4 MB
Unique views 15
Unique downloads 15

Share

Cite as