scholarly journals Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Li Qin
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Rotor Systems ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Nonlinear Features ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal

Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

scholarly journals Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

scholarly journals Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

2019 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
Author(s):  
Weifang Zhang ◽  
Meng Zhang ◽  
Yan Zhao ◽  
Bo Jin ◽  
Wei Dai
Keyword(s):  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Noisy Signals ◽  
Band Limited ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

Research on an Algorithm and System for Estimation of Instantaneous Frequency of Rotating Machinery

2012 ◽  
Vol 452-453 ◽  
pp. 153-159
Author(s):  
Rong Qing Yao
Keyword(s):  
Embedded System ◽  
Instantaneous Frequency ◽  
Vibration Signal ◽  
Average Frequency ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
System Technology ◽  
Mode Decomposition ◽  
Phase Signal ◽  
The Embedded System

Instantaneous frequency is an import parameter to diagnose faults of rotating machinery. This paper puts forward an algorithm based Hilbert-Huang Transformation (HHT) to estimate the instantaneous frequency of rotating machinery and develops an instantaneous cymometer based embedded system technology. In order to estimate instantaneous frequency of rotating machinery, the vibration signal is decomposed into a series of intrinsic mode functions (IMF) first by the method of empirical mode decomposition (EMD), then one of the intrinsic mode functions is analyzed with the Hilbert transformation to acquire an estimate value of instantaneous frequency. An instantaneous cymometer is also described in this paper, which is designed to measure the average frequency and instantaneous frequency of rotating machinery in real time. The average frequency is acquired from measuring the cycle of key-phase signal, and the instantaneous frequency is from the above-mentioned method based HHT. The instantaneous cymometer is consisted of an embedded system, which is connected to a PC with an Ethernet. The embedded system is based on an ARM chip (Samsung S3C4510) A/D conversion, EMD and Hilbert transform are completed on the embedded system, and then the results are compressed and sent to the PC by TCP/IP.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

scholarly journals Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Xie ◽  
Tao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Spatial Information ◽  
Health Management ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

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 Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Heng-di Wang ◽  
Si-er Deng ◽  
Jian-xi Yang ◽  
Hui Liao ◽  
Wen-bo Li
Keyword(s):  
Fault Diagnosis ◽  
Fitness Function ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Penalty Term ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Learning Machine ◽  
Parameter Adaptive

In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

scholarly journals Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

2020 ◽  
Vol 10 (15) ◽  
pp. 5078
Author(s):  
Wenxiao Guo ◽  
Ruiqin Li ◽  
Yanfei Kou ◽  
Jianwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Simulation ◽  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

Sign in / Sign up

Export Citation Format

Share Document