Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal

@inproceedings{Dybaa2013RollingBD,
  title={Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal},
  author={Jacek Dybała and Radosław Zimroz},
  year={2013}
}
Rolling bearing faults are one of the major reasons for breakdown of industrial machinery and bearing diagnosing is one of the most important topics in machine condition monitoring. The main problem in industrial application of bearing vibration diagnostics is the masking of informative bearing signal by machine noise. The vibration signal of the rolling bearing is often covered or concealed by other structural vibrations sources, such as gears. Although a number of vibration diagnostic… CONTINUE READING
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Empirical Mode Decomposition for Fault Diagnosis of Multi-Component Systems

2018 Annual Reliability and Maintainability Symposium (RAMS) • 2018
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References

Publications referenced by this paper.
Showing 1-10 of 31 references

A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings

R Makowski, R. Zimroz
Mechanical Systems and Signal Processing 2013;38(1):65–77 • 2012
View 1 Excerpt

Application of Empirical Mode Decomposition for impulsive signal extraction to detect bearing damage – industrial case study

J Dybała, R. Zimroz
Fakhfakh T et al., editors. Condition monitoring of machinery in nonstationary operations, • 2012
View 1 Excerpt

Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions

J Urbanek, T Barszcz, R Zimroz, J. Antoni
Measurement • 2012
View 2 Excerpts

Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution

T Barszcz, N. Sawalhi
Arch Acoust • 2012
View 1 Excerpt

An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles

M Cocconcelli, L Bassi, C Secchi, C Fantuzzi, R. Rubini
Mech Syst Signal Process 2012;27:667–82 • 2011
View 1 Excerpt

Detection of signal component modulations using modulation intensity distribution

J Urbanek, J Antoni, T. Barszcz
Mech Syst Signal Process 2012;28:399–413 • 2011
View 1 Excerpt