Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Oct 18;21(20):6895.
doi: 10.3390/s21206895.

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

D-M Yang  1
Affiliations

The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis

D-M Yang. Sensors (Basel). .

Abstract

Motor bearings are one of the most critical components in rotating machinery. Envelope demodulation analysis has been widely used to demodulate bearing vibration signals to extract bearing defect frequency components but one of the main challenges is to accurately locate the major fault-induced frequency band with a high signal-to-noise ratio (SNR) for demodulation. Hence, an enhanced fault detection method combining the maximal overlap discrete wavelet packet transform (MODWPT) and the Teager energy adaptive spectral kurtosis (TEASK) denoising algorithms is proposed for identifying the weak periodic impulses. The Teager energy power spectrum (TEPS) defines the sparse representation of the filtered signals of the MODWPT in the frequency domain via the Teager energy operator (TEO); the TEASK helps determine the most informative frequency band for demodulation. The methodology is compared in terms of performance with the fast Kurtogram and the Autogram methods. The simulation and practical application examples have shown that the proposed MODWPT-TEASK method outperforms the above two methods in diagnosing defects of motor bearings.

Keywords: Teager energy adaptive spectral kurtosis; bearing fault detection; maximal overlap discrete wavelet packet transform.

PubMed Disclaimer

Conflict of interest statement

The author declares no conflict of interest.

Figures

Figure 1
Figure 1
Four levels of the MODWPT decomposition of a time series X.
Figure 2
Figure 2
The flowchart of the proposed method for bearing fault detection.
Figure 3
Figure 3
The simulation signal (a) time signal with noise (the red waveform represents the uncontaminated signal), (b) a part of impulse signal, and its (c) amplitude spectrum.
Figure 4
Figure 4
The simulation signal (a) the FK, (b) the SES obtained by the node with highest kurtosis of the FK, (c) the Autogram, (d) the combined SES obtained by the node with highest kurtosis of the Autogram, (e) the TEASK, (f) the TEPS obtained from the node with highest kurtosis of the TEASK. (Red dotted lines: defect frequency component of 105 Hz (fd) and its harmonics; black dash-dot rectangle indicates the optimal frequency band).
Figure 5
Figure 5
Experimental set-up.
Figure 6
Figure 6
Faulty outer-race bearing: (a) time signal; (b) amplitude spectrum.
Figure 7
Figure 7
Faulty outer-race bearing under full-load: (a) the FK; (b) the SES obtained by the node with highest kurtosis of the FK; (c) the Autogram; (d) the combined SES obtained by the node with highest kurtosis of the Autogram; (e) the TEASK; (f) the TEPS obtained from the node with highest kurtosis of the TEASK. (Red dotted lines: outer-race frequency component of 104 Hz (fo) and its harmonics; black dash-dot rectangle indicates the optimal frequency band).
Figure 8
Figure 8
Faulty inner-race bearing: (a) time signal; (b) amplitude spectrum.
Figure 9
Figure 9
Faulty inner-race bearing: (a) the FK; (b) the SES obtained by the node with highest kurtosis of the FK; (c) the Autogram; (d) the combined SES obtained by the node with highest kurtosis of the Autogram; (e) the TEASK; (f) the TEPS obtained from the node with highest kurtosis of the TEASK. (The black dash-dot rectangle indicates the optimal frequency band).
See this image and copyright information in PMC

References

    1. Gangsar P., Tiwari R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mech. Syst. Signal Process. 2020;144:106908. doi: 10.1016/j.ymssp.2020.106908. - DOI
    1. Nandi A., Ahmed H. Condition Monitoring with Vibration Signals. John Wiley & Sons, Ltd.; Hoboken, NJ, USA: 2019. Principles of Rotating Machine Vibration Signals; pp. 17–30. Chapter 2. - DOI
    1. Randall R.B., Antoni J. Rolling element bearing diagnostics—A tutorial. Mech. Syst. Signal Process. 2011;25:485–520. doi: 10.1016/j.ymssp.2010.07.017. - DOI
    1. Liang K., Zhao M., Lin J., Ding C., Jiao J., Zhang Z. A Novel Indicator to Improve Fast Kurtogram for the Health Monitoring of Rolling Bearing. IEEE Sens. J. 2020;20:12252–12261. doi: 10.1109/JSEN.2020.2999107. - DOI
    1. Duan J., Shi T., Zhou H., Xuan J., Zhang Y. Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings. Sensors. 2018;18:1466. doi: 10.3390/s18051466. - DOI - PMC - PubMed

LinkOut - more resources