Application of tentative variational mode decomposition in fault feature detection of rolling element bearing

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.

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

Reliability, Safety and Hazard Assessment for Risk-Based Technologies - Lecture Notes in Mechanical Engineering ◽

10.1007/978-981-13-9008-1_56 ◽

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

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

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19869114 ◽

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.

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Rolling Element Bearing Performance Degradation Assessment Using Variational Mode Decomposition and Gath-Geva Clustering Time Series Segmentation

International Journal of Rotating Machinery ◽

10.1155/2017/2598169 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Yaolong Li ◽

Hongru Li ◽

Bing Wang ◽

Hongqiang Gu

Keyword(s):

Time Series ◽

Decomposition Method ◽

Performance Degradation ◽

Rolling Element Bearing ◽

Local Characteristic ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Time Series Segmentation ◽

Rolling Element ◽

Element Bearing

By focusing on the issue of rolling element bearing (REB) performance degradation assessment (PDA), a solution based on variational mode decomposition (VMD) and Gath-Geva clustering time series segmentation (GGCTSS) has been proposed. VMD is a new decomposition method. Since it is different from the recursive decomposition method, for example, empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristic-scale decomposition (LCD), VMD needs a priori parameters. In this paper, we will propose a method to optimize the parameters in VMD, namely, the number of decomposition modes and moderate bandwidth constraint, based on genetic algorithm. Executing VMD with the acquired parameters, the BLIMFs are obtained. By taking the envelope of the BLIMFs, the sensitive BLIMFs are selected. And then we take the amplitude of the defect frequency (ADF) as a degradative feature. To get the performance degradation assessment, we are going to use the method called Gath-Geva clustering time series segmentation. Afterwards, the method is carried out by two pieces of run-to-failure data. The results indicate that the extracted feature could depict the process of degradation precisely.

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An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

ISA Transactions ◽

10.1016/j.isatra.2019.01.038 ◽

2019 ◽

Vol 91 ◽

pp. 218-234 ◽

Cited By ~ 18

Author(s):

Yao Cheng ◽

Zhiwei Wang ◽

Bingyan Chen ◽

Weihua Zhang ◽

Guanhua Huang

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Rolling Element Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Rolling Element ◽

Element Bearing ◽

Adaptive Noise

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An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2015.03.002 ◽

2015 ◽

Vol 62-63 ◽

pp. 444-459 ◽

Cited By ~ 77

Author(s):

Xiaoming Xue ◽

Jianzhong Zhou ◽

Yanhe Xu ◽

Wenlong Zhu ◽

Chaoshun Li

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Decomposition Method ◽

Ensemble Empirical Mode Decomposition ◽

Rolling Element Bearing ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Rolling Element ◽

Empirical Mode Decomposition Method ◽

Element Bearing

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Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.199-200.895 ◽

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.

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An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Energies ◽

10.3390/en14041079 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1079

Author(s):

Guoping An ◽

Qingbin Tong ◽

Yanan Zhang ◽

Ruifang Liu ◽

Weili Li ◽

...

Keyword(s):

Feature Extraction ◽

Optimization Problem ◽

Rolling Element Bearing ◽

Vibration Signals ◽

Mode Decomposition ◽

Rolling Element ◽

Element Bearing ◽

Bearing Vibration ◽

Optimal Mode ◽

Fault Feature Extraction

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

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