Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition

Cheng, Gang; Chen, Xihui; Li, Hongyu; Li, Peng; Liu, Houguang

doi:10.1016/j.measurement.2016.05.059

Cited by 103 publications

(50 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if there is an abnormal event in the fault signal, modal aliasing in the decomposition process occurs and the EMD method is inefficient. In order to suppress the modal aliasing of the EMD method, the EEMD method was proposed to eliminate the noise of the original signal by adding random Gaussian white noise [51][52][53]. Zhou et al [54] proposed a fault diagnosis method based on the EEMD method for rolling bearing, and the fault frequency was successfully extracted.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It can highlight the signal feature in different scales more comprehensively and sufficiently, and it not only reflects the global information but also gives attention to the detail information of vibration signal. The commonly used feature quantization methods include feature frequency, time domain feature, frequency domain feature, entropy [13], and fractal dimension [14]. Fractal dimension can extract the detail signal features, and it reflects the self-similarity of fine structure and statistical significance; it mainly includes Hausdorff dimension, similarity dimension, box dimension, and information dimension [15,16].…”

Section: Introductionmentioning

confidence: 99%

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

2019

Self Cite

View full text Add to dashboard Cite

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.

show abstract

“…A number of failure diagnosis methods have been used to diagnose the gear faults in variable speed condition, for example, the traditional time frequency analysis methods, self-adaptive signal processing methods, and data driven methods [4][5][6][7][8][9][10][11][12][15][16][17][18][19][20][21][22][23][24][25]. The traditional time frequency analysis methods, such as the short time Fourier transform [14], the Wigner-Ville distribution [10,12], and the wavelet transform [2], will result in spectral aliasing, cross term interference, and low resolution because the vibration signals obtained from the fault gears in practice are nonstationary with low SNR [4].…”

Section: Introductionmentioning

confidence: 99%

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

Zhang

Chai

et al. 2018

Complexity

View full text Add to dashboard Cite

The vibration signals analysis is a very effective and reliable method for detecting the gear failures. Because the vibration signals acquired from the gear in the variable speed condition often contain more useful fault information, the analysis of the gear vibration signals during the variable speed condition has been a hot research topic. In this paper, a method based on the multiscale chirplet path pursuit (MSCPP) and the linear canonical transform (LCT) has been applied to diagnose the gear fault in the variable speed condition for the first time. First, by using the MSCPP method to estimate the instantaneous meshing frequency, the suitable signal segment approximation to the acceleration or deceleration process can be selected. Then, because the LCT is a novel and efficient nonstationary signals analysis tool, the optimal LCT spectrum of the selected signal has been attainted to diagnose the gear faults based on the properties of the LCT. In addition, the simulations and the experimental evaluation are provided to verify the effectiveness of the proposed method.

show abstract

Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition

Cited by 103 publications

References 26 publications

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

Contact Info

Product

Resources

About