scholarly journals Weak fault feature extraction of gear based on KVMD and singular value difference spectrum

2018 ◽  
Vol 211 ◽  
pp. 08001
Author(s):  
Hongkun Li ◽  
Chaoge Wang ◽  
Mengfan Hou ◽  
Rui Yang ◽  
Daolong Tang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Singular Value ◽  
Intrinsic Mode Functions ◽  
Correct Judgment ◽  
Reconstructed Signal ◽  
Gear Fault ◽  
Weak Fault ◽  
Fault Feature Extraction

Gearbox is an important component of many industrial applications. When the gear fault occurs, the vibration signal is characterized by multi-component, multi-frequency modulation, low signal to noise ratio, weak fault characteristics and difficult to extract. This paper proposes a gear fault feature extraction method based on improved variational mode decomposition(VMD) and singular value difference spectrum. Firstly, the method is optimized for the decomposition level K of the VMD algorithm, and an improved method of VMD decomposition layer number K for central frequency screening (KVMD) is proposed. Then, the gear fault vibration signal is decomposed into a series of bandlimited intrinsic mode functions using KVMD. Due to the interference of the noise, it is difficult to make the correct judgment of fault in the spectrum of each mode component. According to the correlation coefficient criterion, the components with larger correlation coefficients are chosen to singular value decomposition. The singular value difference spectrum is obtained, and the effective order of the reconstructed signal is determined from the difference spectrum to denoise the signal; Finally, the processed signal is analyzed by Hilbert envelope. The fault characteristic frequency can be extracted accurately from the envelope spectrum. Through the analysis of the experimental data of gear fault, the results show that the method can effectively reduce the influence of the noise, and accurately realize the extraction of gear fault feature information.

scholarly journals The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Nanfei Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
The Difference ◽  
Fault Feature Extraction

Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

Fault Feature Extraction of Helicopter Rotor System Based on HHT Envelope Demodulation

2013 ◽  
Vol 765-767 ◽  
pp. 2817-2821
Author(s):  
Chen Lu ◽  
Xiao Wei Du ◽  
Hong Mei Liu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rotor System ◽  
Helicopter Rotor ◽  
Center Frequency ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Redundancy Design ◽  
Fault Feature Extraction

Helicopter rotor system (HRS), which is a key component without redundancy design, is of significant importance for flight safety. Working under demanding environment, HRS faults are hard to detect. This paper proposes a new approach based on Hilbert-Huang Transform (HHT) and envelope demodulation to realize HRS fault feature extraction under strong interference. Empirical mode decomposition (EMD) was used to decompose the vibration signal into several intrinsic mode functions (IMFs) first, then, Hilbert transformation was applied to the IMFs to get the envelopes. And at last, fast Fourier transform (FFT) was adopted with the IMF which was closely related to the fault features. This method can avoid the selection of center frequency and filter band in resonance demodulation method, therefore, it has good adaptivity. Two commonly occurring faults in HRS are simulated on a test rig to validate the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method based on HHT envelope demodulation is effective for the HRS fault feature extraction.

scholarly journals GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1946 ◽  
Author(s):  
Jiakai Ding ◽  
Liangpei Huang ◽  
Dongming Xiao ◽  
Xuejun Li
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Optimal Parameter ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Genetic Mutation ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction

The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.

scholarly journals A Hybrid Approach for Weak Fault Feature Extraction of Gearbox

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 16616-16625 ◽  
Author(s):  
Yu Wei ◽  
Minqiang Xu ◽  
Xianzhi Wang ◽  
Wenhu Huang ◽  
Yongbo Li
Keyword(s):  
Feature Extraction ◽  
Hybrid Approach ◽  
Weak Fault ◽  
Fault Feature Extraction

A fractal-dimension-based envelope demodulation for rolling element bearing fault feature extraction from vibration signals

2016 ◽  
Vol 230 (18) ◽  
pp. 3194-3211 ◽  
Author(s):  
Juanjuan Shi ◽  
Ming Liang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Search Algorithm ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Interference Frequency ◽  
Fault Feature Extraction

Vibration analysis has been extensively used as an effective tool for bearing condition monitoring. The vibration signal collected from a defective bearing is, however, a mixture of several signal components including the fault feature (i.e. fault-induced impulses), periodic interferences from other mechanical/electrical components, and background noise. The incipient impulses which excite as well as modulate the resonance frequency of the system are easily masked by compounded effects of periodic interferences and noise, making it challenging to do a reliable fault diagnosis. As such, this paper proposes an envelope demodulation method termed short time fractal dimension (STFD) transform for fault feature extraction from such vibration signal mixture. STFD transform calculation related issues are first addressed. Then, by STFD, the original signal can be quickly transformed into a STFD representation, where the envelope of fault-induced impulses becomes more pronounced whereas interferences are partly weakened due to their morphological appearance differences. It has been found that the lower the interference frequency, the less effect the interference has on STFD representations. When interference frequency keeps increasing, more effects on STFD representations will be resulted. Such effects can be reduced by the proposed kurtosis-based peak search algorithm (KPSA). Therefore, bearing fault signature is kept and interferences are further weakened in the STFD-KPSA representation. The proposed method has been favourably compared with two widely used enveloping methods, i.e. multi-morphological analysis and energy operator, in terms of extracting impulse envelopes from vibration signals obscured by multiple interferences. Its performance has also been examined using both simulated and experimental data.

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