Detection and Characterization of Bearing Faults from the Frequency Domain Features of Vibration

ABSTRACT

The characteristics of vibrations is one which is widely used for the non-intrusive inspection and health monitoring of bearings. However, automated methods, intended for predicting the health status of bearings greatly depend on the features extracted from the vibration signal. In this paper, the ability of frequency domain features such as spectral role-off (SR), median frequency (MF), spectral centroid (SC), dominant frequency (DF), and spectral flux (SF) of the bearing vibration data corresponding to healthy, inner race failure (IRF), roller element defect (RED), and outer race failure (ORF) to identify the state of the bearing is analyzed. The SF, DF, and SC are identified directly from the vibration spectra. The MF and SR are computed from the power spectral density estimate using an analytical method. Before computing the spectrum, the vibration signal is preconditioned with offset elimination and normalization. The normalized data is windowed with Hanning window to suppress the ripples induced in the spectrum during the computation of fast Fourier transform. It has been observed that among the features, MF and SC characterize the status of bearing and the type of faults better than other features. MF is useful to distinguish healthy bearing from IRF and IRF from RED. SC is useful to distinguish IRF from RED and IRF from ORF. The SR, MF, SC, DF, and SF corresponding to the vibrations acquired from normal and faulty bearings differ with a “P” value of 2.22045 × 10⁻¹⁶, ≈ 0, 1.11022 × 10⁻¹⁶, 0.0008, and 2.35957 × 10⁻⁸, respectively, for a level of significance 0.05. SR, MF, and SC are statistically more significant than DF and SF.

KEYWORDS:

• Bearing diagnostics
• Bearing fault detection
• Dominant frequency
• Frequency domain features
• Median frequency
• Spectral centroid
• Spectral flux
• Spectral roll-off
• Vibration signal

ACKNOWLEDGEMENTS

The authors would like to thank Prognostics Center of Excellence, National Aeronautics and Space Administration (NASA), the Center for Intelligent Maintenance System, University of Cincinnati, and Case Western Reserve University Bearing Data Center, Cleveland, Ohio for providing the experimental data.

DISCLOSURE STATEMENT

The authors declare that this article content has no conflicts of interest.

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes on contributors

P. Arun

P. Arun received his MTech degree in embedded systems from National Institute of Electronics and Information Technology, Calicut, India, in 2012. Now he works at SJCET Palai as assistant professor. His current research interests include image processing, fault diagnosis, and vibrational signal processing.

E-mail: [email protected]

S. Abraham Lincon

S. Abraham Lincon received his MTech degree in process control & instrumentation and PhD in instrumentation engineering from Annamalai University, India. Now he works at Annamalai University as professor. His current research interests include image processing, fault diagnosis, vibrational signal processing, process control, and instrumentation system processing.

E-mail: [email protected]

N. Prabhakaran

N. Prabhakaran received his PhD in power electronics and drives from Indian Institute of Technology, Kanpur, (IITK), India, in 1981; MTech in electrical machines from IIT Kharagpur, India, in 1971, and BTech in electrical engineering from Kerala University, India, in 1966. His research interests include power electronics, image processing, and energy management.

E-mail: [email protected]