Mode Selection in Variational Mode Decomposition and Its Application in Fault Diagnosis of Rolling Element Bearing

2019 ◽  
pp. 663-670
Author(s):  
Pradip Yadav ◽  
Shivani Chauhan ◽  
Prashant Tiwari ◽  
S. H. Upadhyay ◽  
Pawan Kumar Rakesh
Keyword(s):  
Fault Diagnosis ◽  
Mode Selection ◽  
Rolling Element Bearing ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

2018 ◽  
Vol 41 (7) ◽  
pp. 1923-1932 ◽  
Author(s):  
Prem Shankar Kumar ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Swarup Kumar Laha
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Rolling Element Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Warping ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Dynamic Time

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing

2011 ◽  
Vol 199-200 ◽  
pp. 895-898
Author(s):  
Hong Fang Yuan ◽  
Peng Wang ◽  
Hua Qing Wang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Wide Band ◽  
Rolling Element Bearing ◽  
Detection Accuracy ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Recognition Experiment ◽  
Rolling Element ◽  
Element Bearing

Because AE (Acoustic Emission) signals in bearing fault monitoring unavoidably mixed various noise which lead to wide band characteristics, in this paper, the collected AE signals are pre-processed by EMD (Empirical Mode Decomposition) algorithm to extract useful information in the concerned frequency range, after that, power spectrum is used to locating analysis and pattern recognition. Experiment show that this method could improve the detection accuracy in rolling element bearing fault diagnosis.

Fault diagnosis for rolling element bearing using variational mode decomposition and l1 trend filtering

2019 ◽  
Vol 234 (1) ◽  
pp. 116-128
Author(s):  
Tingkai Gong ◽  
Xiaohui Yuan ◽  
Xu Wang ◽  
Yanbin Yuan ◽  
Binqiao Zhang
Keyword(s):  
Simulation Analysis ◽  
Regularization Parameter ◽  
Rolling Element Bearing ◽  
Variational Model ◽  
Variational Mode Decomposition ◽  
Outer Race ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Element Bearing

In order to extract the faint fault features of bearings in strong noise background, a method based on variational mode decomposition and l1 trend filtering is proposed in this study. In the variational model, the mode number κ is determined difficulty, thus l1 trend filtering is applied to simplify the frequency spectrum of the original signals. In this case, this parameter can be defined easily. At the same time, a criterion based on kurtosis is used to adaptively select the regularization parameter of l1 trend filtering. The combined approach is evaluated by simulation analysis and the vibration signals of damaged bearings with a rolling element fault, an outer race fault and an inner race fault. The results demonstrate that the hybrid method is effective in detecting the three bearing faults. Moreover, compared with another approach based on multiscale morphology and empirical mode decomposition, the proposed method can extract more bearing fault features.

Sign in / Sign up

Export Citation Format

Share Document