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
. 2022 Oct 24;24(11):1517.
doi: 10.3390/e24111517.

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM

Yinsheng Chen  1   2 , Zichen Yuan  1 , Jiahui Chen  2 , Kun Sun  2
Affiliations

A Novel Fault Diagnosis Method for Rolling Bearing Based on Hierarchical Refined Composite Multiscale Fluctuation-Based Dispersion Entropy and PSO-ELM

Yinsheng Chen et al. Entropy (Basel). .

Abstract

This paper proposes a novel fault diagnosis method for rolling bearing based on hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE) and particle swarm optimization-based extreme learning machine (PSO-ELM). First, HRCMFDE is used to extract fault features in the vibration signal at different time scales. By introducing the hierarchical theory algorithm into the vibration signal decomposition process, the problem of missing high-frequency signals in the coarse-grained process is solved. Fluctuation-based dispersion entropy (FDE) has the characteristics of insensitivity to noise interference and high computational efficiency based on the consideration of nonlinear time series fluctuations, which makes the extracted feature vectors more effective in describing the fault information embedded in each frequency band of the vibration signal. Then, PSO is used to optimize the input weights and hidden layer neuron thresholds of the ELM model to improve the fault identification capability of the ELM classifier. Finally, the performance of the proposed rolling bearing fault diagnosis method is verified and analyzed by using the CWRU dataset and MFPT dataset as experimental cases, respectively. The results show that the proposed method has high identification accuracy for the fault diagnosis of rolling bearings with varying loads and has a good load migration effect.

Keywords: feature extraction; hierarchical refined composite multiscale fluctuation-based dispersion entropy (HRCMFDE); load migration; particle swarm optimization-based extreme learning machine (PSO-ELM); rolling bearing fault diagnosis.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The schematic diagram of hierarchical decomposition at layer n = 3.
Figure 2
Figure 2
The calculation flowchart of HRCMFDE.
Figure 3
Figure 3
Time domain waveforms and spectrograms of WGN and 1/f noise.
Figure 4
Figure 4
Mean and SD of HRCMFDE for WGN and 1/f noise at different m.
Figure 5
Figure 5
Mean and SD of HRCMFDE for WGN and 1/f noise at different c.
Figure 6
Figure 6
Four different entropy values for WGN and 1/f noise: (a,b) HFDE and HRCMFDE; (c,d) MFDE and RCMFDE.
Figure 7
Figure 7
The network structure of ELM.
Figure 8
Figure 8
The algorithm flow chart of PSO-ELM.
Figure 9
Figure 9
The flowchart of the rolling bearing fault diagnosis method based on HRCMFDE and PSO-ELM.
Figure 10
Figure 10
The time domain and spectral diagram of rolling bearing vibration signals with different types in the CWRU dataset.
Figure 11
Figure 11
The fault identification results of the proposed method with load 2 hp in the CWRU dataset.
Figure 12
Figure 12
The fault identification results of different feature extraction methods and PSO-ELM with load 0 hp in the CWRU dataset.
Figure 13
Figure 13
The fault identification accuracy of one load as the training set and another different load as the testing set in the CWRU dataset.
Figure 14
Figure 14
The fault identification accuracy of two loads as the training set and another different load as the testing set in the CWRU dataset.
Figure 15
Figure 15
The fault identification accuracy of two loads as the training set and another two varying loads as the testing set in the CWRU dataset.
Figure 16
Figure 16
The time domain and spectral diagram of rolling bearing vibration signals with different types in the MFPT dataset.
Figure 17
Figure 17
The fault identification results of the proposed method in the MFPT dataset.
Figure 18
Figure 18
The fault identification results of different feature extraction methods and PSO-ELM in the MFPT dataset.
See this image and copyright information in PMC

References

    1. Yan X., Jia M. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing. 2018;313:47–64. doi: 10.1016/j.neucom.2018.05.002. - DOI
    1. Chen Y., Zhang T., Zhao W., Luo Z., Lin H. Rotating machinery fault diagnosis based on improved multiscale amplitude-aware permu-tation entropy and multiclass relevance vector machine. Sensors. 2019;19:4542. doi: 10.3390/s19204542. - DOI - PMC - PubMed
    1. Rai A., Upadhyay S. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol. Int. 2016;96:289–306. doi: 10.1016/j.triboint.2015.12.037. - DOI
    1. Liu R., Yang B., Zio E., Chen X. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 2018;108:33–47. doi: 10.1016/j.ymssp.2018.02.016. - DOI
    1. Yang L., Chen H. Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Comput. Appl. 2018;31:4463–4478. doi: 10.1007/s00521-018-3525-y. - DOI

LinkOut - more resources