Rolling bearing fault detection based on local characteristic-scale decomposition and teager energy operator

Liu Hongmei1, Chen Yu2

School of Reliability and Systems Engineering, Beihang University, Beijing, China

1Corresponding author


Received 1 October 2017; accepted 10 October 2017



Abstract. In this paper, a rolling bearing fault detection method based on Local Characteristic‑scale Decomposition (LCD) and Teager Energy operator (TEO) is proposed. Vibration signals is related to the bearing fault. However, the vibration signal of rolling bearing is nonlinear and has multiple components, which makes it difficult to analyze the signals by using traditional method such as the fast Fourier transform (FFT). LCD, a recently developed signal decomposition method, is especially capable for dealing with the complex signal by decomposing it into several intrinsic scale components (ISC). Furthermore, to extract fault diagnosis of the components, we used TEO to demodulate each ISC. The energy of fault feature frequencies was extracted as fault vector. The result shows that the method successfully diagnoses bearing fault.

Keywords: rolling bearing, fault diagnosis, local characteristic-scale decomposition, teager energy operator.


[1]        He L. Research on Rolling Element Bearing Diagnosis Method Using EMD. Dalian University of Technology, 2012.

[2]        Li G., Li F. Application of STFT in the field of aero-engine vibration signal processing. Measurement and Control Technology, Vol. 32, Issue 4, 2013, p. 45‑49.

[3]        Khan A. F. Condition monitoring of Rolling Element Bearing, A Comparative Study of Vibration Based Techniques of Windsor. Ontario, 1990.

[4]        Wang Q. H., Zhang X. B. Application of fractal theory to fault diagnosis for hydraulic pump. Journal of Dalian Maritime University, 2004.

[5]        Kaiser J. F. On a simple algorithm to calculate the “energy” of a signal. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’90), Vol. 1, 1990, p. 381‑384.

[6]        Potamianos A., Maragos P. A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation. Signal Processing, Vol. 37, Issue 1, 1994, p. 95‑120.

Cite this article

Hongmei Liu, Yu Chen Rolling bearing fault detection based on local characteristic‑scale decomposition and teager energy operator. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 126‑129.


© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533