Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers
Chhaya Grover1, Neelam Turk2

1Chhaya Grover*, Department of Electronics Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India.
2Dr. Neelam Turk, J. C. Bose University of Science and Technology, YMCA, Faridabad, India.

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 350-358 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4836029320/2020©BEIESP | DOI: 10.35940/ijeat.C4836.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: 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.
Keywords: Empirical mode decomposition, Ensemble classifiers, Statistical features, Vibration signature analysis