Warped Variational Mode Decomposition With Application to Vibration Signals of Varying-Speed Rotating Machineries

2019 ◽  
Vol 68 (8) ◽  
pp. 2755-2767 ◽  
Author(s):  
Shiqian Chen ◽  
Yang Yang ◽  
Xingjian Dong ◽  
Guanpei Xing ◽  
Zhike Peng ◽  
...  
Keyword(s):  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Rotating Machineries

scholarly journals Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition for Multifault Diagnosis of a Multistage Reducer

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Feng Li ◽  
Xinyu Pang ◽  
Zhaojian Yang
Keyword(s):  
Planetary Gear ◽  
Vibration Signal ◽  
Spur Gear ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
The Difference ◽  
Simulation Signal ◽  
Frequency Demodulation ◽  
Frequency Modulations

Multistage reducer vibration signals have complicated spectral structures owing to the amplitude and frequency modulations of gear damage-induced vibrations and the multiplicative amplitude modulation effect caused by time-varying vibration transfer paths (in the case of local gear damage) when the multistage reducer contains both planetary and spur gears. Moreover, the difference between the vibration energies of these gears increases the difficulty of fault feature extraction when multiple failures occur in the reducer. As the meshing frequency of each gear group often varies significantly, variational mode decomposition can be performed to decompose the vibration signal according to frequency, enabling separation of the vibration signals of the spur and planetary gears. The common fault features of these gears can be extracted from the spectrum of the amplitude demodulation envelope. To verify the effectiveness of this method, we first analyzed a simulation signal, and then utilized the experimental signals from a laboratory multistage reducer for verification. In the multistage reducer simulation, we considered the amplitude and frequency modulation of the gear damage and transfer paths. In the experimental verification, we processed local faults (broken teeth) and uniform faults (uniform wear) on the sun gear and the spur gear of the planetary gear separately.

scholarly journals Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 96 ◽  
Author(s):  
Xiaoming Xue ◽  
Chaoshun Li ◽  
Suqun Cao ◽  
Jinchao Sun ◽  
Liyan Liu
Keyword(s):  
Random Forests ◽  
Classification Model ◽  
Permutation Entropy ◽  
Rolling Element Bearings ◽  
Variational Mode Decomposition ◽  
Statistical Classification ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Single Scale ◽  
Rolling Element

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

scholarly journals Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Xiaoyang Bi ◽  
Shuqian Cao ◽  
Daming Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Engine Valve ◽  
Mode Decomposition ◽  
Reconstructed Signal

The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

scholarly journals Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 470
Author(s):  
Zijian Guo ◽  
Mingliang Liu ◽  
Huabin Qin ◽  
Bing Li
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Recognition Rate ◽  
Rotating Machinery ◽  
Bootstrap Sample ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery.

scholarly journals Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

2019 ◽  
Vol 13 (1) ◽  
pp. 4477-4492
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
L. M. Hee ◽  
Z. A. B. Ahmad
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Processing Method ◽  
Mode Number ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Learning Machine

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.  

scholarly journals Variational Mode Decomposition Denoising Combined with the Euclidean Distance for Diesel Engine Vibration Signal

2017 ◽  
Author(s):  
Gang Ren ◽  
Jide Jia ◽  
Xiangyu Jia ◽  
Jiajia Han
Keyword(s):  
Diesel Engine ◽  
Probability Density Function ◽  
Probability Density ◽  
Euclidean Distance ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Connecting Rod ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Simulation Signal

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map

2020 ◽  
Vol 26 (21-22) ◽  
pp. 1886-1897 ◽  
Author(s):  
Jialing Zhang ◽  
Jimei Wu ◽  
Bingbing Hu ◽  
Jiahui Tang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
The Self ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Map ◽  
Mode Decomposition ◽  
Self Organizing

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.

scholarly journals Weak Degradation Characteristics Analysis of UAV Motors Based on Laplacian Eigenmaps and Variational Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 524 ◽  
Author(s):  
Xiaohong Wang ◽  
Wenhui Fan ◽  
Xinjun Li ◽  
Lizhi Wang
Keyword(s):  
Background Noise ◽  
Degradation Process ◽  
Variational Mode Decomposition ◽  
Laplacian Eigenmaps ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Validation Experiment ◽  
Characteristics Analysis ◽  
The Stability ◽  
Stability And Accuracy

Brushless direct current (BLDC) motors are the source of flight power during the operation of rotary-wing unmanned aerial vehicles (UAVs), and their working state directly affects the safety of the whole system. To predict and avoid motor faults, it is necessary to accurately understand the health degradation process of the motor before any fault occurs. However, in actual working conditions, due to the aerodynamic environmental conditions of the aircraft flight, the background noise components of the vibration signals characterizing the running state of the motor are complex and severely coupled, making it difficult for the weak degradation characteristics to be clearly reflected. To address these problems, a weak degradation characteristic extraction method based on variational mode decomposition (VMD) and Laplacian Eigenmaps (LE) was proposed in this study to precisely identify the degradation information in system health data, avoid the loss of critical information and the interference of redundant information, and to optimize the description of a motor’s degradation process despite the presence of complex background noise. A validation experiment was conducted on a specific type of motor under operation with load, to obtain the degradation characteristics of multiple types of vibration signals, and to test the proposed method. The results proved that this method can improve the stability and accuracy of predicting motor health, thereby helping to predict the degradation state and to optimize the maintenance strategies.

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