scholarly journals Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 44871-44882 ◽  
Author(s):  
Zhijian Wang ◽  
Gaofeng He ◽  
Wenhua Du ◽  
Jie Zhou ◽  
Xiaofeng Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

2016 ◽  
Vol 231 (4) ◽  
pp. 864-870 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Decomposition Method ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Component Amplitude

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

scholarly journals An improved variational mode decomposition method and its application in diesel engine fault diagnosis

2018 ◽  
Vol 20 (6) ◽  
pp. 2363-2378 ◽  
Author(s):  
Jianmin Mei ◽  
Gang Ren ◽  
Jide Jia ◽  
Xiangyu Jia ◽  
Jiajia Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Decomposition Method ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

2020 ◽  
pp. 147592172097085
Author(s):  
Xingxing Jiang ◽  
Jun Wang ◽  
Changqing Shen ◽  
Juanjuan Shi ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Efficient Extraction ◽  
Low Efficiency

Variational mode decomposition has been widely applied to machinery fault diagnosis during these years. However, it remains difficult to set proper hyperparameters for the variational mode decomposition, including number of decomposed modes, initial center frequencies, and balance parameter. Moreover, the low efficiency of the existing variational mode decomposition methods hinders their applications to practical diagnostic task. This article proposes an adaptive and efficient variational mode decomposition method after thoroughly investigating its convergence property characteristic. A convergent tendency phenomenon is discovered and is explained mathematically for the first time. Motivated by the convergent tendency phenomenon, the proposed method rapidly and adaptively determines the number and the optimal initial center frequencies of signal latent modes with the guidance of the convergent tendencies of the initial center frequencies changing from small to large. In the proposed method, the number of decomposed modes and initial center frequencies are not hyperparameters that require to be set in advance any more, but are parameters learned from the analyzed signals. The determined parameters enable efficient extraction of the main latent modes contained in the analyzed signals. Therefore, the proposed variational mode decomposition method represents a major improvement in parameter adaption and decomposition efficiency over the existing variational mode decomposition methods. In the application for bearing fault diagnosis, the faulty modes are selected adaptively and the corresponding balance parameters are further optimized efficiently. Two experimental cases validate the proposed method and its superiority over the existing variational mode decomposition methods and the classical fast spectral kurtosis in bearing fault diagnosis.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
Author(s):  
Zhijian Wang ◽  
Junyuan Wang ◽  
Wenhua Du
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Optimal Number ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Multiple Fault ◽  
Mode Decomposition ◽  
Fault Features ◽  
Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

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