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feature extraction of Bearing Vibration Signals Using Second Generation Wavelet and Spline-Based Local Mean Decomposition

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Journal of Shanghai Jiaotong university(Science) 2015年第1期20卷 56-60页

作者：文成玉董良金欣 College of Communication Engineering Chengdu University of Information Technology

In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generation wavelet denoising,the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions(PFs).Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns.Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault.This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.

关键词： second generation wavelet transform local mean decomposition(LMD) feature extraction fault diagnosis

Ship recognition based on HRRP via multi-scale sparse preserving method

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Journal of Systems Engineering and Electronics 2024年第3期35卷 599-608页

作者： YANG Xueling ZHANG Gong SONG Hu College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Lab of Radar Imaging and Microwave Photonics Ministry of EducationNanjing 210016China Nanjing Marine Radar Institute Nanjing 210016China

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

关键词： ship target recognition high-resolution range profile(HRRP) multi-scale fusion kernel sparse preserving projection(MSFKSPP) feature extraction dimensionality reduction

feature extraction for classification of different weather conditions

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Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 339-346页

作者： Xudong ZHAO Peng LIU Jiafeng LIU Xianglong TANG School of Computer Science Harbin Institute of TechnologyHarbin 150001China

Classification of different weather conditions provides a first step support for outdoor scene modeling,which is a core component in many different applications of outdoor video analysis and computer vision.features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification.In this paper,features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed.Based on the autocorrelation of each pixel’s intensities over time,two temporal features are used for coarse classification of weather conditions according to their visual effects.On the other hand,features are extracted for fine classification of video clips with rain and snow.The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features,by using C-SVM as the classifier.

关键词： feature extraction classification rain snow illumination variation weather condition autocorrelation function

Research on feature extraction of Remnant Particles of Aerospace Relays

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Chinese Journal of Aeronautics 2007年第3期20卷 253-259页

作者： WANG Shu-juan GAO Hong-liang ZHAI Guo-fu Department of Electrical Engineering Harbin Institute of Technology Harbin 150001 China

The existence of remnant particles, which significantly reduce the reliability of relays, is a serious problem for aerospace relays. The traditional method for detecting remnant particles-particle impact noise detection （PIND）-can be used merely to detect the existence of the particle; it is not able to provide any information about the particles＇ material. However, information on the material of the particles is very helpful for analyzing the causes of remnants. By analyzing the output acoustic signals from a PIND tester, this paper proposes three feature extraction methods： unit energy average pulse durative time, shape parameter of signal power spectral density （PSD）, and pulse linear predictive coding coefficient sequence. These methods allow identified remnants to be classified into four categories based on their material. Furthermore, we prove the validity of this new method by processing P1ND signals from actual tests.

关键词： feature extraction aerospace relays remnant particles particle impact noise detection

Unsupervised noise-robust feature extraction for aerial image classification

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Science China(Technological Sciences) 2020年第8期63卷 1406-1415页

作者： LIANG Ye LU Shuai WENG Rui HAN ChengZhe LIU Ming School of Astronautics Harbin Institute of TechnologyHarbin 150001China Academy of Art Harbin University of Science and TechnologyHarbin 150001China Academy of Software and Microelectronics Harbin University of Science and TechnologyHarbin 150001China

The rich data provided by satellites and unmanned aerial vehicles bring opportunities to directly model aerial image features by extracting their spatial and structural patterns.Although convolutional autoencoders(CAEs)have been attained a remarkable performance in ideal aerial image feature extraction,they are still challenging to extract information from noisy images which are generated from capture and transmission.In this paper,a novel CAE-based noise-robust unsupervised learning method is proposed for extracting high-level features accurately from aerial images and mitigating the effect of noise.Different from conventional CAEs,the proposed method introduces the noise-robust module between the encoder and the decoder.Besides,several pooling layers in CAEs are replaced by convolutional layers with stride=2.The performance of feature extraction is evaluated by the prediction accuracy and the accuracy loss in image classification experiments.A 5-classes aerial optical scene and a 9-classes hyperspectral image(HSI)data set are utilized for optical image and HSI feature extraction,respectively.Highlevel features extracted from aerial images are utilized for image classification by a linear support vector machine(SVM)classifier.Experimental results indicate that the proposed method improves the classification accuracy for noisy images(Gaussian noise 2Dσ=0.1,3Dσ=60)in both optical images(2D 87.5%)and HSIs(3D 85.6%)compared with the traditional CAE(2D 78.6%,3D 84.2%).The accuracy loss in classification experiments increases with the increment of noise.Compared with the traditional CAE(2D 15.7%,3D 11.8%),the proposed method shows the lower classification accuracy loss in experiments(2D 0.3%,3D 6.3%).The proposed unsupervised noise-robust feature extraction method attains desirable classification accuracy in ideal input and enhances the feature extraction capability from noisy input.

关键词： aerial image classification convolutional autoencoder feature extraction noise-robust

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

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Frontiers of Mechanical Engineering 2017年第3期12卷 406-419页

作者： Yun KONG Tianyang WANG Zheng LI Fulei CHU Department of Mechanical Engineering Tsinghua University Beijing 100084 China

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics： Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum （SK-TWES） in the paper. Firstly, the spectral kurtosis （SK） and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

关键词： wind turbine planet gear fault feature extraction spectral kurtosis time wavelet energy spectrum

Improved method for the feature extraction of laser scanner using genetic clustering

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Journal of Systems Engineering and Electronics 2008年第2期19卷 280-285页

作者： Yu Jinxia Cai Zixing Duan Zhuohua Coll. of Information ScienCe and Engineering Central South Univ. Changsha 410083 P. R. China Coll. of Computer Science and Technology Henan Polytechnic Univ. Jiaozuo 454003 P. R. China Dept. of Computer Science Shaoguan Univ. Shaoguan 512003 P. R. China

feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.

关键词： laser scanner feature extraction weighted fuzzy clustering validation index genetic algorithm.

A comparative study on ApEn,SampEn and their fuzzy counterparts in a multiscale framework for feature extraction

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2010年第4期11卷 270-279页

作者： Guo-liang XIONG Long ZHANG He-sheng LIU Hui-jun ZOU Wei-zhong GUO School of Mechatronie Engineering East China Jiaotong University Nanchang 330013 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics Shangrao Normal University Shangrao 334001 China

feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.

关键词： Fault diagnosis Bearing Multiscale entropy feature extraction Support vector machines (SVMs)

Auditory-model-based feature extraction Method for Mechanical Faults Diagnosis

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Chinese Journal of Mechanical Engineering 2010年第3期23卷 391-397页

作者： LI Yungong ZHANG Jinping DAI Li ZHANG Zhanyi LIU Jie Sehool of Mechanical Engineering and Automation Northeastern University Shenyang 110004 China School of Mechanical Engineering Shenyang University of Chemical Technology Shenyang 110142 China

It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory（EA） model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.

关键词： faults diagnosis feature extraction auditory model early auditory model