A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation

Song, Yukun; Zeng, Shengkui; Ma, Jianxun; Guo, Jianbin

doi:10.1016/j.measurement.2017.12.029

Cited by 83 publications

(38 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fault diameter of these three bearing components is 0.007 inches (0.01778 cm). Based on the parameters listed in Table 2 and the formulas in literature [45], we can calculate the corresponding characteristic frequencies and they are listed in Table 3.…”

Section: Case Studiesmentioning

confidence: 99%

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Zhou

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

show abstract

Section: Case Studiesmentioning

confidence: 99%

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Zhou

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…EWT has better performance than EMD in restraining the endpoint effect and model mixture. The properties of self-adaptive and wavelet transform are integrated into EWT [20,21,31].…”

Section: Symbolic Analysis Of Ewtmentioning

confidence: 99%

“…Consider that WT has a stronger ability of local frequency domain analysis for signals, another improved method, denoted as the empirical wavelet transform (EWT), was developed by Gilles [20]. In EWT, the frequency information of signal is extracted by the fast Fourier transform and a proper wavelet filter bank is established according to segmentations of the Fourier spectrum to decompose the pure vibration modes without mixture [21]. In many cases, the components obtained from EWT always have more advantages in signal analysis.All methods mentioned above are always denoted as self-adaptive decomposition methods.…”

mentioning

confidence: 99%

Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Shi

Zhao

et al. 2019

Entropy

View full text Add to dashboard Cite

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

show abstract

“…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. However, the EEMD method only overcomes the modal aliasing phenomenon of the EMD method to a certain extent, and still has the problems of the modal aliasing phenomenon and low efficiency.…”

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

A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation

Cited by 83 publications

References 25 publications

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

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

Contact Info

Product

Resources

About