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bearing fault diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

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Energy Engineering 2022年第2期119卷 665-680页

作者： Xiaolei Li Beijing Goldwind Science&Goldwind Huineng Technology Co. Ltd.Beijing100176China

Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment,a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network(IADRSN)is proposed.Firstly,the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets.Then,a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established.To improve the ability of fault feature extraction of the model,the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer.To further improve the anti-noise ability of the model,the first layer of the model was set as the Drop-block layer.Finally,the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results.The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

关键词： bearing fault diagnosis improved residual shrinkage network noise immunity

Attention mechanism based multi-scale feature extraction of bearing fault diagnosis

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Journal of Systems Engineering and Electronics 2023年第5期34卷 1359-1367页

作者： LEI Xue LU Ningyun CHEN Chuang HU Tianzhen JIANG Bin College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816China

Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.

关键词： bearing fault diagnosis multiple conditions atten-tion mechanism multi-scale data deep belief network(DBN)

WDBM: Weighted Deep Forest Model Based bearing fault diagnosis Method

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Computers, Materials & Continua 2022年第9期72卷 4741-4754页

作者： Letao Gao Xiaoming Wang Tao Wang Mengyu Chang Department of Computer Science City University of Hong KongHong Kong999077China School of Computer and Software Engineer Xihua UniversityChengdu610039China Nanjing University of Aeronautics and Astronautics Nanjing210008China McGill University MontrealH3G 1Y2Canada

In the research field of bearing fault diagnosis,classical deep learning models have the problems of too many parameters and high computing cost.In addition,the classical deep learning models are not effective in the scenario of small data.In recent years,deep forest is proposed,which has less hyper parameters and adaptive depth of deep model.In addition,weighted deep forest(WDF)is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision tree.In this paper,weighted deep forest model-based bearing fault diagnosis method(WDBM)is proposed.The WDBM is regard as a novel bearing fault diagnosis method,which not only inherits the WDF’s advantages-strong robustness,good generalization,less parameters,faster convergence speed and so on,but also realizes effective diagnosis with high precision and low cost under the condition of small samples.To verify the performance of the WDBM,experiments are carried out on Case Western Reserve University bearing data set(CWRU).Experiments results demonstrate that WDBM can achieve comparative recognition accuracy,with less computational overhead and faster convergence speed.

关键词： Deep forest bearing fault diagnosis weights

bearing fault diagnosis Based on Graph Formulation and Graph Convolutional Network

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Journal of Dynamics, Monitoring and Diagnostics 2023年第4期2卷 252-261页

作者： Xin Wang Wenjin Zhou Xiaodong Li Research and Development Center of Smart Information and Communications Technologies Shanghai Advanced Research InstituteChinese Academy of SciencesNo.99 Haike RoadShanghai 201210China Key Laboratory of Noise and Vibration Research Institute of AcousticsChinese Academy

bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery.Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio,image,and video.However,these methods falter when confronting non-Euclidean datasets,notably graph representations.In response,here we introduce an innovative approach harnessing the graph convolutional network(GCN)to analyze graph data derived from vibration signals related to bearing faults.This enhances the precision and reliability of fault diagnosis.Our methodology initiates by deriving a periodogram from the unprocessed vibration signals.Subsequently,this periodogram is mapped into a graph format,upon which the GCN is engaged for classification purposes.We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets.Within these experiments,an accelerometer chronicles vibration signals across varying load conditions.We probe into the diagnostic accuracy rates across diverse loads and signal-to-noise ratios.Furthermore,a comparative evaluation of our method against several established algorithms delineated in this study is undertaken.Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.

关键词： bearing fault diagnosis deep learning graph convolutional network

CVRgram for Demodulation Band Determination in bearing fault diagnosis under Strong Gear Interference

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Journal of Dynamics, Monitoring and Diagnostics 2022年第4期1卷 237-250页

作者： Pengda Wang Dezun Zhao Dongdong Liu Lingli Cui Beijing Key Laboratory of Advanced Manufacturing Technology Faculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124

fault-related resonance frequency band extraction-based demodulation methods are widely used for bearing diagnostics.However,due to the high peaks of strong gear meshing interference,the classical band selection methods have poor performance and cannot work well for bearing fault type detection.As such,the CVRgram-based bearing fault diagnosis method is proposed in this paper.In the proposed method,inspired by the conditional variance(CV)index and root mean square(RMS),a novel index,named the CV/root mean square(CVR),is first proposed.The CVR index has high robustness for the interference of non-Gaussian or Gaussian noise and has the ability to determine the center frequency of the weak bearing fault-related resonance frequency band under strong interference.Secondly,motived by the Kurtogram,the CVRgram algorithm is developed for adaptively determining the optimal filtering parameters.Finally,the CVRgram-based bearing fault diagnosis method under strong gear meshing interference is proposed.The performance of the CVRgram-based method is verified by both the simulation signal and the experiment signal.The comparison analysis with the Kurtogram,Protrugram,and CVgram-based method shows that the proposed technique has a much better ability for bearing fault detection under strong noise interference.

关键词： bearing fault diagnosis CVRgram gear meshing interference resonance frequency band detection

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Journal of Harbin Institute of Technology(New Series) 2020年第1期27卷 1-9页

作者： Jinbao Zhang Yongqiang Zhao Lingxian Kong Ming Liu School of Mechatronics Engineering Harbin Institute of Technology

bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

关键词： bearing fault diagnosis multi⁃scale permutation entropy morphology similarity distance

bearing fault diagnosis Based on Empirical Wavelet Transform and Singular Value Decomposition

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Bearing Fault Diagnosis Based on Empirical Wavelet Transform...

Investigation on bearing Weak fault diagnosis under Colored Noise

作者： Han-shuo LI Bin JIAO Wei-tian LIN School of Electrical Shanghai DianJi University

Aiming at the problem of noise in the signal in bearing fault diagnosis,a diagnosis method based on empirical wavelet transform and singular value decomposition was proposed.Firstly,the vibration signal of the fault bearing outer ring was decomposed by empirical wavelet transform,and then the signal was reconstructed by singular value decomposition.The combination of the two not only decomposed the frequencies contained in the respective components,but also eliminated noise components and had high reliability in analyzing signals.The spectrum of the signal was analyzed to identify the fault signal in the Hilbert spectrum and compared to the theoretically calculated frequency.The experimental results show that the method can accurately determine bearing faults.

关键词： bearing fault diagnosis Empirical wavelet transform Singular value decomposition

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Investigation on Bearing Weak Fault Diagnosis under Colored ...

An Improved Multiscale Stochastic Resonance Method for bearing fault diagnosis

第32届中国控制与决策会议

作者： Shan Shijie Wang Kai Qie Xuliang Zheng Dan Dai Xueqing Shi Jiale Faculty of Mechanical and Precision Instrument Engineering Xi’an University of Technology

For a bearing weak fault,it contains both periodic signals and a large amount of noise.The energy of the noise is large and its components are complex.It is difficult to obtain the early weak fault characteristics of bearing.In this paper,a typical Duffing system is improved by the generalized time transform and the method of changing the initial phase angle of built-in periodic strategy force.The developed method can be used to detect signal in a wider frequency band and the problem of misjudgment arising from Duffing system has been solved as well.According to the characteristics of the displacement output value variance of Duffing system,a method of bearing weak fault diagnosis is proposed.A high-intensity nonlinear colored noise has been introduced to disturb simulated weak fault signal,and then the weak fault signal has been diagnosed successfully by the proposed method.Under the condition of strong noise a kind of bearing inner ring fault has been diagnosed successfully.The proposed methods in this paper have certain theoretical and practical reference value.

关键词： bearing fault diagnosis Duffing System Displacement Output Value Variance Colored Noise

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An Improved Multiscale Stochastic Resonance Method for Beari...