Planned intervention: On Wednesday April 3rd 05:30 UTC Zenodo will be unavailable for up to 2-10 minutes to perform a storage cluster upgrade.
Published February 29, 2020 | Version v1
Journal article Open

Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

  • 1. Department of Electronics Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India.
  • 2. J. C. Bose University of Science and Technology, YMCA, Faridabad, India.
  • 1. Publisher

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.

Files

C4836029320 (1).pdf

Files (542.3 kB)

Name Size Download all
md5:ba5637be4015537175e2b4a0e0f6b4f2
542.3 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2249-8958 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
C4836029320/2020©BEIESP