A new autocorrelation-based strategy for multiple fault feature extraction from gearbox vibration signals

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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A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

Shock and Vibration ◽

10.1155/2021/8824901 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Peng Chen ◽

Xiaoqiang Zhao ◽

HongMei Jiang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

White Noise ◽

Gaussian White Noise ◽

Rolling Bearing ◽

K Nearest Neighbor ◽

Vibration Signals ◽

Diagnosis Method ◽

Feature Information ◽

Fault Feature Extraction

In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

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FAULT FEATURE EXTRACTION OF HYDRO-GENERATOR VIBRATION SIGNALS BASED ON WAVELET SHRINKAGE

Wavelet Analysis and Its Applications ◽

10.1142/9789812796769_0147 ◽

2003 ◽

Author(s):

TAO SUN ◽

T. S. HUANG ◽

MING LI ◽

F. X. SUN ◽

J. D. XIANG

Keyword(s):

Feature Extraction ◽

Wavelet Shrinkage ◽

Vibration Signals ◽

Fault Feature Extraction

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An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Energies ◽

10.3390/en14041079 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1079

Author(s):

Guoping An ◽

Qingbin Tong ◽

Yanan Zhang ◽

Ruifang Liu ◽

Weili Li ◽

...

Keyword(s):

Feature Extraction ◽

Optimization Problem ◽

Rolling Element Bearing ◽

Vibration Signals ◽

Mode Decomposition ◽

Rolling Element ◽

Element Bearing ◽

Bearing Vibration ◽

Optimal Mode ◽

Fault Feature Extraction

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

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Gear Fault Classification Using Kernel Discriminant Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.321-323.1556 ◽

2006 ◽

Vol 321-323 ◽

pp. 1556-1559

Author(s):

Wei Hua Li ◽

Kang Ding ◽

Tie Lin Shi ◽

Guang Lan Liao

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Principal Component ◽

Operating Conditions ◽

Fault Classification ◽

Kernel Principal Component Analysis ◽

Kernel Discriminant Analysis ◽

Vibration Signals ◽

Gearbox Vibration ◽

Gear Fault

This paper presents a study of KDA(kernel discriminant analysis) in gearbox failure feature extraction and classification. Experimental gearbox vibration signals measured from normal, gear small spall, gear severe spall and gear wear operating conditions are analyzed using either KPCA(kernel principal component analysis) or KDA as the feature extraction and fault classification methods. Experiment results indicate the effectiveness and thesuperiority of KDA for gear fault classification over KPCA.

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A New Morphological Filter for Fault Feature Extraction of Vibration Signals

IEEE Access ◽

10.1109/access.2019.2912898 ◽

2019 ◽

Vol 7 ◽

pp. 53743-53753 ◽

Cited By ~ 5

Author(s):

Jianbo Yu ◽

Tianzhong Hu ◽

Haiqiang Liu

Keyword(s):

Feature Extraction ◽

Morphological Filter ◽

Vibration Signals ◽

Fault Feature Extraction

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A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

Applied Sciences ◽

10.3390/app11114996 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4996

Author(s):

Gang Yao ◽

Yunce Wang ◽

Mohamed Benbouzid ◽

Mourad Ait-Ahmed

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Vibration Signal ◽

Optimization Techniques ◽

Fault Classification ◽

Vibration Signals ◽

Kernel Parameter ◽

Diagnosis Method ◽

The Impact ◽

Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

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A new fault feature extraction method of rotating machinery based on finite sample function

Noise & Vibration Worldwide ◽

10.1177/0957456520948273 ◽

2020 ◽

pp. 095745652094827

Author(s):

Feng Miao ◽

Rongzhen Zhao

Keyword(s):

Feature Extraction ◽

Separation Performance ◽

Finite Sample ◽

Finite Support ◽

Vibration Signals ◽

Score Functions ◽

Feature Extraction Method ◽

Fault Features ◽

Simulation Results ◽

Fault Feature Extraction

Feature extraction plays a crucial role in the diagnosis of rotating machinery’s faults. In order to separate different fault vibration signals from measured mixtures and diagnose the fault features of the machine effectively according to the separated signals, a blind source separation (BSS) method using kernel function based on finite support samples was proposed. The method is stronger adaptability to the score functions estimated according to finite support observed signal samples. The simulation results prove that the proposed BSS algorithm is able to separate mixed signals that contain both sub-Gaussian and super-Gaussian sources. It is shown that the algorithm has better separation performance when compared with other BSS ones. The results of an experiment under the rotor’s composite fault states with rub-impact fault and unbalance fault show that this method has higher efficiency and accuracy.

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Feature extraction of gearbox vibration signals based on EEMD and sample entropy

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2013.6816305 ◽

2013 ◽

Author(s):

Xiaoping Chen ◽

Chunfang Yin ◽

Weixing He

Keyword(s):

Feature Extraction ◽

Sample Entropy ◽

Vibration Signals ◽

Gearbox Vibration

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A Fast Signal Estimation Method Based on Probability Density Functions for Fault Feature Extraction of Rolling Bearings

Applied Sciences ◽

10.3390/app9183768 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3768

Author(s):

Shijun Li ◽

Weiguo Huang ◽

Juanjuan Shi ◽

Xingxing Jiang ◽

Zhongkui Zhu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Computational Complexity ◽

Probability Density Functions ◽

Density Functions ◽

Rolling Bearings ◽

Vibration Signals ◽

Transient Component ◽

Fast Estimation ◽

Fault Feature Extraction

Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation–maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency.

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