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
. 2018 Jun 14;11(6):1009.
doi: 10.3390/ma11061009.

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Yong Lv  1   2 , Rui Yuan  3   4 , Tao Wang  5   6 , Hewenxuan Li  7 , Gangbing Song  8
Affiliations

Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE

Yong Lv et al. Materials (Basel). .

Abstract

Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.

Keywords: CEEMDAN; early fault diagnosis; health degradation monitoring; improved MMSE; rolling bearing; smoothed coarse graining process.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
The illustration of EEMD.
Figure 2
Figure 2
The illustration of the conventional coarse graining process.
Figure 3
Figure 3
The illustration of proposed smoothed coarse graining process.
Figure 4
Figure 4
The proposed health degradation monitoring and early fault diagnosis scheme of a rolling bearing.
Figure 5
Figure 5
(a) Time domain plots of IMFs of EEMD; and (b) time domain plots of IMFs of CEEMDAN.
Figure 6
Figure 6
(a) Frequency domain plots of effective IMFs of EEMD; and (b) frequency domain plots of effective IMFs of CEEMDAN.
Figure 7
Figure 7
(a) MMSE values of the 1st second simulated trivariate signal adopting the conventional coarse graining process; and (b) MMSE values of the 1st second simulated trivariate signal adopting the smoothed coarse graining process.
Figure 8
Figure 8
1st MMSE values of all subsequences of simulated trivariate faulty rolling bearing signal.
Figure 9
Figure 9
(a) The schematic diagram of the apparatus; and (b) a picture of the apparatus.
Figure 10
Figure 10
(a) IMFs obtained by EEMD; and (b) IMFs obtained by CEEMDAN.
Figure 11
Figure 11
MMSE values of all scales via the proposed health degradation monitoring approach of the 100th set of data.
Figure 12
Figure 12
Comparative study of the six different methods. (a) The MSE values of the optimal IMF obtained by EEMD; (b) The MSE values of the optimal IMF obtained by CEEMDAN; (c) The 2nd MMSE values of effective IMFs obtained by EEMD; (d) The 1st MMSE values of effective IMFs obtained by EEMD; (e) The 1st MMSE values of effective IMFs obtained by CEEMDAN (conventional coarse graining process within MMSE); and (f) The 1st MMSE values of effective IMFs obtained by CEEMDAN (smoothed coarse graining process within MMSE).
Figure 13
Figure 13
The 1st MMSE values of effective IMFs obtained by CEEMDAN (smoothed coarse graining process with MMSE).
Figure 14
Figure 14
Time and frequency domain plots of the 530th set of data (denoting the signal around the 88.3th h), during the beginning of Phase 2 of the rolling bearing wear-out process.
Figure 15
Figure 15
(a) IMFs obtained by EEMD; and (b) IMFs obtained by CEEMDAN.
Figure 16
Figure 16
Frequency domain plots of the original signal and the reconstructed signals. (a) Frequency domain plot of the original early weak fault signal; (b) frequency domain plot of the reconstructed signal processed by WPD; (c) frequency domain plot of the reconstructed signal processed by EEMD; and (d) frequency domain plot of the reconstructed signal processed by CEEMDAN.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Lei Y., Lin J., He Z., Zuo M.J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechan. Syst. Signal Process. 2013;35:108–126. doi: 10.1016/j.ymssp.2012.09.015. - DOI
    1. Cerrada M., Sánchez R.V., Li C., Pacheco F., Cabrera D., de Oliveira J.V., Vásquez R.E. A review on data-driven fault severity assessment in rolling bearings. Mechan. Syst. Signal Process. 2018;99:169–196. doi: 10.1016/j.ymssp.2017.06.012. - DOI
    1. Li B., Chow M.Y., Tipsuwan Y., Hung J.C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 2000;47:1060–1069. doi: 10.1109/41.873214. - DOI
    1. Zhao M., Lin J., Xu X., Lei Y. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds. Sensors. 2013;13:10856–10875. doi: 10.3390/s130810856. - DOI - PMC - PubMed
    1. Yuan J., Wang Y., Peng Y., Wei C. Weak fault detection and health degradation monitoring using customized standard multiwavelets. Mechan. Syst. Signal Process. 2017;94:384–399. doi: 10.1016/j.ymssp.2017.03.005. - DOI

LinkOut - more resources