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feature extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

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Chinese Journal of Mechanical Engineering 2015年第1期28卷 96-105页

作者： GAO Huizhong LIANG Lin CHEN Xiaoguang XU Guanghua School of Mechanical Engineering Xi'an Jiaotong University The 705 Research Institute China Shipbuilding Industry Corporation Key Laboratory for Modern Design and Rotor-Bearing System of Education Ministry Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

关键词： time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction

feature extraction of overlapping hevea leaves:A comparative study

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Information Processing in Agriculture 2018年第2期5卷 234-245页

作者： Sule T.Anjomshoae Mohd Shafry Bin Mohd Rahim Department of Software Engineering Faculty of ComputingUniversiti Teknologi Malaysia81310 SkudaiJohor BahruMalaysia

Automation of rubber tree clone classification has inspired research into new methods of leaf feature extraction.In current practice,rubber clone inspectors has been using several leaf features to identify clone types.One of the unique features of rubber tree leaf is palmate leaflets.This characteristic generates different leaflet positions,where the leaves are overlapping or separated.In this research,we propose keypoint extraction and line detection methods to extract shape and axil(angle between petioles)features of leaflet positions.The results of keypoint extraction methods,namely,SIFT,Harris,and FAST,were compared and discussed for shape feature extraction.Next,Hough transformation and boundary-tracing methods were compared to identify the suitable axil detection method.The evaluation result demonstrates the proper keypoint extraction method for shape context and the clear advantages of Hough Transformation in accuracy of angle detection.

关键词： Rubber tree leaves feature extraction SIFT Harris FAST Hough transformation Boundary extraction

feature extraction of jujube fruit wrinkle based on the watershed segmentation

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International Journal of Agricultural and Biological Engineering 2017年第4期10卷 165-172页

作者： Zhang Junxiong Ma Qingqing Li Wei Xiao Tingting College of Engineering China Agricultural UniversityBeijing 100083China

The degree of surface wrinkles on a dried jujube fruit(Ziziphus jujuba Mill.)is an important quality grading criterion.The aim of this research was to propose an image processing method based on the watershed segmentation to extract the wrinkle features of jujube fruits.Original images of jujube fruit taken under cyan light were transformed into grayscale images.The noise in these images was then removed by morphological reconstruction.The H-minima extended transformation was used to label the foreground of jujube fruit images after reconstruction,and the labeled foreground regions were segmented by a distance transform-based watershed algorithm.Then,the grayscale images were filtered with a local range filter.The segmentation function was obtained using the minima imposition method.Finally,a watershed segmentation was used to extract the wrinkle features of jujube fruits.Experiments on 304 images of jujube fruit showed that the accuracy of wrinkle-based grading obtained by the algorithm was 92.11%,which proved that this method could be used to classify jujube wrinkles.

关键词： feature extraction watershed segmentation image processing jujube fruit wrinkle quality grading

feature extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

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Computers, Materials & Continua 2023年第1期74卷 1437-1455页

作者： S.Prabhakaran R.Annie Uthra J.Preetharoselyn Department of Computational Intelligence SRM Institute of Science and TechnologyChengalpattu603203India Department of Electrical Engineering SRM Institute of Science and TechnologyChengalpattu603203India

Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.

关键词： Photovoltaic panels deep learning defect feature extraction RMVDM

feature extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 1041-1049页

作者： CHEN Xiaoguang LIANG Lin XU Guanghua LIU Dan School of Mechanical Engineering Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University

The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

关键词： feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment

feature extraction and Dimensionality Reduction of Arc Sound under Typical Penetration Status in Metal Inert Gas Welding

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Chinese Journal of Mechanical Engineering 2012年第2期25卷 293-298页

作者： LIU Lijun LAN Hu ZHENG Hongyan JIAN Xiaoxia Ningbo Institute of Technology Zhejiang UniversityNingbo 315100China School of Material Science ＆ Engineering Harbin University of Science and TechnologyHarbin 150080China Rongcheng College Harbin University of Science and TechnologyRongcheng 264300China

Arc sound is well known as the potential and available resource for monitoring and controlling of the weld penetration status,which is very important to the welding process quality control,so any attentions have been paid to the relationships between the arc sound and welding parameters.Some non-linear mapping models correlating the arc sound to welding parameters have been established with the help of neural networks.However,the research of utilizing arc sound to monitor and diagnose welding process is still in its infancy.A self-made real-time sensing system is applied to make a study of arc sound under typical penetration status,including partial penetration,unstable penetration,full penetration and excessive penetration,in metal inert-gas（MIG） flat tailored welding with spray transfer.Arc sound is pretreated by using wavelet de-noising and short-time windowing technologies,and its characteristics,characterizing weld penetration status,of time-domain,frequency-domain,cepstrum-domain and geometric-domain are extracted.Subsequently,high-dimensional eigenvector is constructed and feature-level parameters are successfully fused utilizing the concept of primary principal component analysis（PCA）.Ultimately,60-demensional eigenvector is replaced by the synthesis of 8-demensional vector,which achieves compression for feature space and provides technical supports for pattern classification of typical penetration status with the help of arc sound in MIG welding in the future.

关键词： metal inert gas welding penetration arc sound feature extraction dimensionality reduction

feature extraction Approach for Defect Inspection in Eddy Current Pulsed Thermography

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Journal of Electronic Science and Technology 2021年第4期19卷 390-400页

作者： Pei-Pei Zhu Li-Bing Bai Yu-Hua Cheng with the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 610054

The eddy current pulsed thermography(ECPT)technique is a research focus in the non-destructive testing(NDT)area for defect inspection.Defect feature extraction for defect information analysis in ECPT is limited by image contrast,heat diffusion,background interference,etc.In this paper,a defect feature extraction approach in ECPT has been proposed to improve the quality of defect features,which is based on image partition,local sparse component evaluation,and feature fusion.This method can extract complete defect features by enhancing the defect area and removing background interference,such as noises and heating coil.Two typical steel specimens are utilized to testify the validity of the proposed approach.Compared with other three common feature extraction algorithms in ECPT,the proposed method can reserve more complete defect features and suppress more background interference.

关键词： Eddy current thermography feature extraction machine learning non-destructive testing(NDT).

Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

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Science China(Information Sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based methods.The experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

feature extraction of partial discharge in low-temperature composite insulation based on VMD-MSE-IF

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 301-312页

作者： Xi Chen Xiao Shao Xin Pan Gaochao Luo Maoqiang Bi Tianyan Jiang Kang Wei School of Electrical and Electronic Engineering Chongqing University of TechnologyChongqingChina Chongqing Wanzhou District Municipal Facilities Maintenance and Management Center ChongqingChina Meta Platforms Inc Menlo ParkCaliforniaUSA

Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

关键词： feature extraction pattern recognition