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

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 406-419 doi: 10.1007/s11465-017-0419-0

Abstract:

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis hasfeature.Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extractioncollected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the featureextraction and fault diagnosis for the planet gear with a localized defect.

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

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Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 264-291 doi: 10.1007/s11465-018-0472-3

Abstract:

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries inHigh-value machineries require condition monitoring and fault diagnosis to guarantee their designed functionsResearch on machinery Fault diagnostics has grown rapidly in recent years.The review discusses the special contributions of Chinese scholars to machinery fault diagnostics.On the basis of the review of basic theory of machinery fault diagnosis and its practical applications

Keywords： fault diagnosis     fault mechanism     feature extraction     signal processing     intelligent diagnostics    

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Application of wavelet scalogram in feature extraction of acoustic emission signal

Xiao Siwen,Liao Chuanjun,Li Xuejun

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 69-75

Abstract: By analyzing the characteristics and feature extraction of typical AE signals, the paper applies waveletscalogram for fault diagnosis based on AE technique, and presents the wavelet scalogram analysis methodWhen applying wavelet scalogram for fault diagnosis of rolling bearings based on AE techniques, the resultssimulations and experimental research prove that wavelet scalogram can be used for condition monitoring and fault

Keywords： wavelets scalogram     acoustic emission     feature extraction     fault diagnosis     rolling bearing    

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signal analysis based on general parameterized time--frequency transform and its application in the featureextraction of a rotary machine

Peng ZHOU, Zhike PENG, Shiqian CHEN, Yang YANG, Wenming ZHANG

Frontiers of Mechanical Engineering 2018, Volume 13, Issue 2,   Pages 292-300 doi: 10.1007/s11465-017-0443-0

Abstract: development of large rotary machines for faster and more integrated performance, the condition monitoring and faultTF) pattern of the vibration signal from the rotary machine often contains condition information and faultfeature, the methods based on TF analysis have been widely-used to solve these two problems in the industrialA multi-component instantaneous frequency (IF) extraction method is proposed based on it.

Keywords： rotary machines     condition monitoring     fault diagnosis     GPTFT     SCI    

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: Existing fault diagnosis methods usually assume that there are balanced training data for every machineHowever, the collection of fault signals is very difficult and expensive, resulting in the problem ofIt will degrade the performance of fault diagnosis methods significantly.SuperGraph feature learning is proposed in this paper.diagnosis towards imbalanced training dataset through graph feature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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Acoustic fault signal extraction via the line-defect phononic crystals

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1,   Pages 10-10 doi: 10.1007/s11465-021-0666-y

Abstract: Rotating machine fault signal extraction becomes increasingly important in practical engineering applicationsHowever, fault signals with low signal-to-noise ratios (SNRs) are difficult to extract, especially atthe early stage of fault diagnosis.As a result, fault signals with high SNRs can be obtained for fault feature extraction.

Keywords： phononic crystals     line-defect     fault signal extraction     acoustic enhancement    

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Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 38-42

Abstract: It can extract nonlinear feature components of samples.However, feature extraction for one sample requires that kernel functions between training samples andSo, the size of training sample set affects the efficiency of feature extraction.It is supposed that in feature space the eigenvectors may be linearly expressed by a part of trainingIKPCA extracts feature components of one sample efficiently, only based on kernel functions between nodes

Keywords： KPCA(Kernel PCA)     IKPCA(Improved KPCA)     feature extraction     feature space    

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Adaptive construction of multiwavelet basis function and its applications for mechanical fault diagnosis

He Zhengjia,Sun Hailiang,Zi Yanyang

Strategic Study of CAE 2011, Volume 13, Issue 10,   Pages 83-92

Abstract:

The faults initiated in operation (i.e. incipient fault) with the obscureHence, fault diagnosis and prognosis of incipient faults have been the difficulty and focus of the researchstudied, and the results showed the effectiveness of enhancement of weak dynamic signals and features extraction

Keywords： mechanical fault diagnosis     principle of inner product transform     adaptive basis function     multiwaveletdenoising     fault feature extraction    

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Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 357-366 doi: 10.1007/s11465-017-0423-4

Abstract:

Given the weak early degradation characteristic information during early fault evolution in gearboxA weak characteristic information extraction based on µ-SVD and local mean decompositionsignificantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

Keywords： generator gearbox     µ-singular value decomposition     local mean decomposition     weak characteristic information extraction     early fault warning    

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Feature extraction from slice data for reverse engineering

ZHANG Yingjie, LU Shangning

Frontiers of Mechanical Engineering 2007, Volume 2, Issue 1,   Pages 25-31 doi: 10.1007/s11465-007-0004-z

Abstract: A new approach to feature extraction for slice data points is presented.

Keywords： feasibility     corresponding     B-spline     pre-determined tolerance     extraction    

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Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 1, doi: 10.1007/s11465-022-0725-z

Abstract: energy operator (EO) and its variants have received considerable attention in the field of bearing faultfeature detection.feature nonlinearly and effectively.Third, the intrinsic manifolds are weighted to recover the fault-related transients.feature than the existing methods, including HOEOs, the weighting HOEO fusion, the fast Kurtogram, and

Keywords： higher order energy operator     fault diagnosis     manifold learning     rolling element bearing     information    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: The scarcity of fault data and a large amount of normal data in practical use pose great challenges tofault detection algorithms.Therefore, a fault detection method based on self-supervised feature learning was proposed to addressA comprehensive comparison study was also conducted with various feature extractors and unary classifiersmodel can detect progressive faults very quickly and achieve improved results for comparison without feature

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 8,   Pages 50-55

Abstract: Then the fusion features in the complex feature space is extracted by using complex PCA (CPCA).

Keywords： feature fusion     linear discriminant analysis (LDA)     feature extraction     face recognition    

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Wavelet design for extracting weak fault feature based on lifting scheme

JIANG Hong-kai, WANG Zhong-sheng, HE Zheng-jia

Frontiers of Mechanical Engineering 2006, Volume 1, Issue 2,   Pages 199-203 doi: 10.1007/s11465-006-0009-z

Abstract: Weak fault features of mechanical signals are usually immersed in noisy signals.A new wavelet method based on lifting scheme to match weak fault characteristics is proposed.engineering results confirm that the proposed method is better than other wavelet methods for extracting weak faultfeature.the position and time that weak signal singularity occurs are clearly found, and slight rub-impact fault

Keywords： misalignment     imbalance     particular     position     mechanical    

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Blind identification of threshold auto-regressive model for machine fault diagnosis

LI Zhinong, HE Yongyong, CHU Fulei, WU Zhaotong

Frontiers of Mechanical Engineering 2007, Volume 2, Issue 1,   Pages 46-49 doi: 10.1007/s11465-007-0007-9

Abstract: hidden Markov model (HMM) to determine the auto-regressive (AR) coefficients for each interval used for featureextraction, with the HMM as a classifier.The fault diagnoses during the speed-up and speed-down processes for rotating machinery have been successfully

Keywords： speed-up     classifier     practical     extraction     experiment    

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Title Author Date Type Operation

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

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

Journal Article

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN

Journal Article

Application of wavelet scalogram in feature extraction of acoustic emission signal

Xiao Siwen,Liao Chuanjun,Li Xuejun

Journal Article

signal analysis based on general parameterized time--frequency transform and its application in the featureextraction of a rotary machine

Peng ZHOU, Zhike PENG, Shiqian CHEN, Yang YANG, Wenming ZHANG

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Acoustic fault signal extraction via the line-defect phononic crystals

Journal Article

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Journal Article

Adaptive construction of multiwavelet basis function and its applications for mechanical fault diagnosis

He Zhengjia,Sun Hailiang,Zi Yanyang

Journal Article

Weak characteristic information extraction from early fault of wind turbine generator gearbox

Xiaoli XU, Xiuli LIU

Journal Article

Feature extraction from slice data for reverse engineering

ZHANG Yingjie, LU Shangning

Journal Article

Iterative HOEO fusion strategy: a promising tool for enhancing bearing fault feature

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

A Face Recognition Based on Fusion Features Extraction From Two Kinds of Projection

Zhang Shengliang,Xu Yong,Yang Jian,Yang Jingyu

Journal Article

Wavelet design for extracting weak fault feature based on lifting scheme

JIANG Hong-kai, WANG Zhong-sheng, HE Zheng-jia

Journal Article

Blind identification of threshold auto-regressive model for machine fault diagnosis

LI Zhinong, HE Yongyong, CHU Fulei, WU Zhaotong

Journal Article