Fault Diagnosis of Flywheel Bearing Based on Parameter Optimization Variational Mode Decomposition Energy Entropy and Deep Learning

Energy ◽  
2021 ◽  
pp. 122108
Author(s):  
Deqiang He ◽  
Chenyu Liu ◽  
Zhenzhen Jin ◽  
Rui Ma ◽  
Yanjun Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Variational Mode Decomposition ◽  
Energy Entropy ◽  
Mode Decomposition

Fault Diagnosis of Bearing Based on Variational Mode Decomposition and Deep Learning

2021 ◽  
Author(s):  
Jianguo Cui ◽  
Shan Tang ◽  
Xiao Cui ◽  
Jinglin Wang ◽  
Mingyue Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

A novel fault diagnosis method based on improved adaptive variational mode decomposition, energy entropy, and probabilistic neural network

2020 ◽  
Vol 44 (1) ◽  
pp. 121-132 ◽  
Author(s):  
Shengjie Zhang ◽  
Huimin Zhao ◽  
Junjie Xu ◽  
Wu Deng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Screening Method ◽  
Maximum Output ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Energy Entropy ◽  
Mode Decomposition ◽  
Fault Recognition

To improve the accuracy of bearing fault recognition, a novel bearing fault diagnosis (PAVMD-EE-PNN) method based on parametric adaptive variational mode decomposition (VMD), energy entropy, and probabilistic neural network (PNN) is proposed in this paper. In view of the effect of VMD on signal decomposition effect affected by the number of preset decomposition modes, a central frequency screening method is proposed to determine the number of decomposition modes of the VMD method. The parametric adaptive VMD method is used to decompose the bearing fault signal into a series of intrinsic mode function (IMF) components. The energy entropy of IMF components is calculated to form an eigenvector, which is input into the PNN model for training to obtain a fault recognition model with maximum output probability. The actual bearing vibration data are obtained and used to test and verify the effectiveness of the PAVMD-EE-PNN method. The experimental results show that the PAVMD-EE-PNN method can effectively and accurately identify the fault type, and the fault recognition effect is better than contrast fault diagnosis methods.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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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