scholarly journals A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3323 ◽  
Author(s):  
Huimin Zhao ◽  
Shaoyan Zuo ◽  
Ming Hou ◽  
Wei Liu ◽  
Ling Yu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Minimum Length ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Curve Method ◽  
Adaptive Signal ◽  
Maximum Minimum ◽  
Motor Bearing

Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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.

Study on a Motor Bearing Fault Diagnosis Method Using Improved EWT Based on Scale Space Threshold Method

2018 ◽  
Vol 19 (4) ◽  
Author(s):  
Huimin Zhao ◽  
Shaoyan Zuo ◽  
Jian Fang ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Fourier Spectrum ◽  
Vibration Signal ◽  
Scale Space ◽  
Adaptive Segmentation ◽  
Threshold Method ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Motor Bearing

Abstract Empirical Wavelet Transform (EWT) is a novel non-stationary signal analysis method that can effectively identify different mode components in signals. However, due to the lack of processing noise and unstable signals caused by the Fourier spectrum adaptive segmentation problem, an improved EWT (FCMEWT) method based on the scale space threshold method and fuzzy C-means is proposed to decompose the vibration signal into an empirical mode with physical meaning. The FCMEWT method firstly scales the spectrum of the original vibration signal, and then uses the fuzzy C-means method to classify the spectrum in order to obtain the spectrum division interval. The vibration signal is decomposed into a set of intrinsic mode functions (IMFs) components, which are performed Hilbert transform for extracting the frequency of each component through the power spectrum. Finally, Pearson correlation coefficient between each IMF component and the original signal is calculated to obtain the correlation coefficient threshold in order to determine the final IMF component. In order to verify the effectiveness of FCMEWT method, the vibration signal motor bearing is selected in this paper. The FCMEWT method is compared with the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods. The results show that the FCMEWT method can effectively solve the problem of Fourier spectrum segmentation in the EWT method, takes on better adaptive segmentation characteristics, and can effectively extract fault feature frequency of motor bearing. The fault diagnosis method can not only effectively extract motor bearing fault characteristics, but also has better diagnosis result than EMD and EEMD methods.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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