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

关键词： bearing fault diagnosis deep learning graph convolutional network

bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network

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High-Speed Railway 2024年第2期2卷 92-100页

作者： Zhongmei Wang Pengxuan Nie Jianhua Liu Jing He Haibo Wu Pengfei Guo College of Railway Transportation Hunan University of TechnologyZhuzhou 412007China Wuxi Maimurun Environmental Technology Co. LtdWuxi 214000China

Multisensor data fusionmethod can improve the accuracy of bearing fault diagnosis,in order to address the problems of single-sensor data types and the insufficient exploration of redundancy and complementarity between different modal data in most existing multisensor data fusion methods for bearing fault diagnosis,a bearing fault diagnosis method based on a Multiple-Constraint Modal-Invariant Graph Convolutional Fusion Network(MCMI-GCFN)is proposed in this ***,a Convolutional Autoencoder(CAE)and Squeeze-and-Excitation Block(SE block)are used to extract features of raw current and vibration ***,the model introduces source domain classifiers and domain discriminators to capture modal invariance between different modal data based on domain adversarial training,making use of the redundancy and complementarity between multimodal ***,the spatial aggregation property of Graph Convolutional Neural Networks(GCN)is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic ***,the validation is conducted on the public bearing damage current and vibration dataset from Paderborn *** experimental results showed that the delivered fusion method achieved a bearing fault diagnosis accuracy of 99.6%,which was about 9%–11.4%better than that with nonfusion methods.

关键词： bearing fault diagnosis Data fusion Domain adversarial training GCN

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 *** practical applications,bearings often work at various rotational speeds as well as load ***,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further ***,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 *** key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention *** benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this ***,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 *** addition,the classical deep learning models are not effective in the scenario of small *** recent years,deep forest is proposed,which has less hyper parameters and adaptive depth of deep *** 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 *** this paper,weighted deep forest model-based bearing fault diagnosis method(WDBM)is *** 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 *** 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

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 ***,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 *** such,the CVRgram-based bearing fault diagnosis method is proposed in this *** 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 *** 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 ***,motived by the Kurtogram,the CVRgram algorithm is developed for adaptively determining the optimal filtering ***,the CVRgram-based bearing fault diagnosis method under strong gear meshing interference is *** performance of the CVRgram-based method is verified by both the simulation signal and the experiment *** 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 *** 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 ***,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after ***,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 ***,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,*** categories were considered for the two experiments and high classification accuracies were *** 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...

Stochastic resonance of coupled time-delayed system with fluctuation of mass and frequency and its application in bearing fault diagnosis

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 ***,the vibration signal of the fault bearing outer ring was decomposed by empirical wavelet transform,and then the signal was reconstructed by singular value *** 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 *** spectrum of the signal was analyzed to identify the fault signal in the Hilbert spectrum and compared to the theoretically calculated *** 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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Journal of Central South University 2021年第9期28卷 2931-2946页

作者： ZHANG Gang WANG Hui ZHANG Tian-qi School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

The stochastic resonance behavior of coupled stochastic resonance(SR)system with time-delay under mass and frequency fluctuations was ***,the approximate system model of the time-delay system was obtained by the theory of small time-delay ***,the random average method and Shapiro-Loginov algorithm were used to calculate the output amplitude ratio of the two *** simulation analysis shows that increasing the time-delay and the input signal amplitude appropriately can improve the output response of the ***,the system is applied to bearing fault diagnosis and compared with the stochastic resonance system with random mass and random *** experimental results show that the coupled SR system taking into account the actual effect of time-delay and couple can more effectively extract the frequency of the fault signal,and thus realizing the diagnosis of the fault signal,which has important engineering application value.

关键词： stochastic resonance bearing fault diagnosis the fluctuation of mass and frequency coupled time-delayed system

An Improved Multiscale Stochastic Resonance Method for bearing fault diagnosis

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An Improved Multiscale Stochastic Resonance Method for Beari...