Non-intrusive detection and characterisation of bearing faults from the temporal features of vibration

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ABSTRACT

The behaviour of vibrations is widely used for non-intrusive inspection and health monitoring of bearings. However, automated methods, intended for predicting the bearing status, greatly depend on the features extracted from the vibration. Generally, time domain features are computationally simpler than frequency and time–frequency domain features. In this paper, the ability of time domain features to characterise the type of bearing fault is analysed. Types of bearing faults considered are healthy, inner race failure (IRF), roller element defect (RED) and outer race failure (ORF).The features being analysed are standard error (SE), absolute deviation of SE from the reciprocal of number of samples (β), entropy (E), variance (µ), standard deviation (SD), peak amplitude (PA), RMS, crest factor (CRF), impulse factor (IF), shape factor (SHF), energy and clearance factor (CLF). Among these, SE, ‘β’, IF and SHF characterise the status of bearing and the type of faults better than other features. The SE, ‘β’, IF and SHF corresponding to the vibrations acquired from normal and faulty bearings differ with a ‘P’ value of, 7.13866 × 10⁻²³,7.06651 × 10^−23, ≈ 0 and ≈ 0, respectively. These features can be used to distinguish defective bearings with 100% sensitivity and 94.73% specificity.

KEYWORDS:

• Autocorrelation
• bearing fault detection
• bearing diagnostics
• entropy
• standard error
• time domain features
• 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.

Declaration of interest

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.

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.

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, energy management and image processing.