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Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows
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作者 Haomiao Wang Jinxi Wang +4 位作者 Qingmei Sui Faye Zhang Yibin Li Mingshun Jiang Phanasindh Paitekul 《Structural Durability & Health Monitoring》 EI 2024年第2期91-110,共20页
Due to their robust learning and expression ability for complex features,the deep learning(DL)model plays a vital role in bearing fault diagnosis.However,since there are fewer labeled samples in fault diagnosis,the de... Due to their robust learning and expression ability for complex features,the deep learning(DL)model plays a vital role in bearing fault diagnosis.However,since there are fewer labeled samples in fault diagnosis,the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields,which limits the diagnostic performance.To solve this problem,a novel transfer residual Swin Transformer(RST)is proposed for rolling bearings in this paper.RST has 24 residual self-attention layers,which use the hierarchical design and the shifted window-based residual self-attention.Combined with transfer learning techniques,the transfer RST model uses pre-trained parameters from ImageNet.A new end-to-end method for fault diagnosis based on deep transfer RST is proposed.Firstly,wavelet transform transforms the vibration signal into a wavelet time-frequency diagram.The signal’s time-frequency domain representation can be represented simultaneously.Secondly,the wavelet time-frequency diagram is the input of the RST model to obtain the fault type.Finally,our method is verified on public and self-built datasets.Experimental results show the superior performance of our method by comparing it with a shallow neural network. 展开更多
关键词 rolling bearing fault diagnosis TRANSFORMER self-attention mechanism
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Multi-view feature fusion for rolling bearing fault diagnosis using random forest and autoencoder 被引量:6
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作者 Sun Wenqing Deng Aidong +4 位作者 Deng Minqiang Zhu Jing Zhai Yimeng Cheng Qiang Liu Yang 《Journal of Southeast University(English Edition)》 EI CAS 2019年第3期302-309,共8页
To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the ... To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis. 展开更多
关键词 multi-view features feature fusion fault diagnosis rolling bearing machine learning
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Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing
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作者 Xingang WANG Chao WANG 《Mechanical Engineering Science》 2019年第2期1-7,共7页
Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy,a fault diagnosis method based on Xgboost algorithm feature extraction ... Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy,a fault diagnosis method based on Xgboost algorithm feature extraction is proposed.When the Xgboost algorithm classifies features,it generates an order of importance of the input features.The time domain features were extracted from the vibration signal of the rolling bearing,the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition.Firstly,the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy.Then,Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis.Finally,important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy.The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost. 展开更多
关键词 fault diagnosis rolling bearing xgboost feature extraction support VECTOR machine
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Deep Residual Joint Transfer Strategy for Cross-Condition Fault Diagnosis of Rolling Bearings 被引量:1
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作者 Songjun Han Zhipeng Feng 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第1期42-51,共10页
Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturb... Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturbing components,and the variation of operating conditions leads to unbalanced data distribution among different conditions.Although intelligent diagnosis methods based on deep learning have been intensively studied,it is still challenging to diagnose rolling bearing faults with small amounts of samples.To address the above issue,we introduce the deep residual joint transfer strategy method for the cross-condition fault diagnosis of rolling bearings.One-dimensional vibration signals are pre-processed by overlapping feature extraction techniques to fully extract fault characteristics.The deep residual network is trained in training tasks with sufficient samples,for fault pattern classification.Subsequently,three transfer strategies are used to explore the generalizability and adaptability of the pre-trained models to the data distribution in target tasks.Among them,the feature transferability between different tasks is explored by model transfer,and it is validated that minimizing data differences of tasks through a dual-stream adaptation structure helps to enhance generalization of the models to the target tasks.In the experiments of rolling bearing faults with unbalanced data conditions,localized faults of motor bearings and planet bearings are successfully identified,and good fault classification results are achieved,which provide guidance for the cross-condition fault diagnosis of rolling bearings with small amounts of training data. 展开更多
关键词 fault diagnosis feature transferability rolling bearing transfer strategy wind turbine
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Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network
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作者 Guangjun Jiang Dezhi Li +4 位作者 Ke Feng Yongbo Li Jinde Zheng Qing Ni He Li 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第4期275-289,共15页
Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment.In response to the problem of frequent faults in rolling bearings,this paper designs a... Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment.In response to the problem of frequent faults in rolling bearings,this paper designs a rolling bearing fault diagnosis method based on convolutional capsule network(CCN).More specifically,the original vibration signal is converted into a two-dimensional time–frequency image using continuous wavelet transform(CWT),and the feature extraction is performed on the two-dimensional time–frequency image using the convolution layer at the front end of the network,and the extracted features are input into the capsule network.The capsule network converts the extracted features into vector neurons,and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis.Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method.The results show that the CCN has good diagnostic capability under different working conditions,even in the presence of noise and insufficient samples,compared to other models.This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios. 展开更多
关键词 continuous wavelet transform convolutional capsule network fault diagnosis rolling bearings
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Long-Range Dependencies Learning Based on Nonlocal 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis
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作者 Huan Wang Zhiliang Liu Ting Ai 《Journal of Dynamics, Monitoring and Diagnostics》 2022年第3期148-159,共12页
In the field of data-driven bearing fault diagnosis,convolutional neural network(CNN)has been widely researched and applied due to its superior feature extraction and classification ability.However,the convolutional o... In the field of data-driven bearing fault diagnosis,convolutional neural network(CNN)has been widely researched and applied due to its superior feature extraction and classification ability.However,the convolutional operation could only process a local neighborhood at a time and thus lack the ability of capturing long-range dependencies.Therefore,building an efficient learning method for long-range dependencies is crucial to comprehend and express signal features considering that the vibration signals obtained in a real industrial environment always have strong instability,periodicity,and temporal correlation.This paper introduces nonlocal mean to the CNN and presents a 1D nonlocal block(1D-NLB)to extract long-range dependencies.The 1D-NLB computes the response at a position as a weighted average value of the features at all positions.Based on it,we propose a nonlocal 1D convolutional neural network(NL-1DCNN)aiming at rolling bearing fault diagnosis.Furthermore,the 1D-NLB could be simply plugged into most existing deep learning architecture to improve their fault diagnosis ability.Under multiple noise conditions,the 1D-NLB improves the performance of the CNN on the wheelset bearing data set of high-speed train and the Case Western Reserve University bearing data set.The experiment results show that the NL-1DCNN exhibits superior results compared with six state-of-the-art fault diagnosis methods. 展开更多
关键词 convolutional neural network fault diagnosis long-range dependencies learning rolling bearing
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Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis 被引量:1
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作者 Jian Di Leilei Wang 《Journal of Computer and Communications》 2018年第7期41-53,共13页
Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive... Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters. 展开更多
关键词 fault diagnosis rolling bearing Deep Auto-Encoder NETWORK CAPSO Algorithm feature Extraction
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Fault Diagnosis Method of Rolling Bearing Based on MSCNN-LSTM
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作者 Chunming Wu Shupeng Zheng 《Computers, Materials & Continua》 SCIE EI 2024年第6期4395-4411,共17页
Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently.To address the problem that the insufficient fault feature extraction ability of traditional fa... Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently.To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios,a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network(MSCNN)and Long Short-Term Memory(LSTM)fused with attention mechanism is proposed.To adaptively extract the essential spatial feature information of various sizes,the model creates a multi-scale feature extraction module using the convolutional neural network(CNN)learning process.The learning capacity of LSTM for time information sequence is then used to extract the vibration signal’s temporal feature information.Two parallel large and small convolutional kernels teach the system spatial local features.LSTM gathers temporal global features to thoroughly and painstakingly mine the vibration signal’s characteristics,thus enhancing model generalization.Lastly,bearing fault diagnosis is accomplished by using the SoftMax classifier.The experiment outcomes demonstrate that the model can derive fault properties entirely from the initial vibration signal.It can retain good diagnostic accuracy under variable load and noise interference and has strong generalization compared to other fault diagnosis models. 展开更多
关键词 bearing fault diagnosis convolutional neural network short-long-term memory network feature fusion
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Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction 被引量:12
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作者 Debiao Meng Hongtao Wang +3 位作者 Shiyuan Yang Zhiyuan Lv Zhengguo Hu Zihao Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第1期543-558,共16页
In a wind turbine,the rolling bearing is the critical component.However,it has a high failure rate.Therefore,the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the hig... In a wind turbine,the rolling bearing is the critical component.However,it has a high failure rate.Therefore,the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment.In this study,the failure form and the corresponding reason for the failure are discussed firstly.Then,the natural frequency and the characteristic frequency are analyzed.The Empirical Mode Decomposition(EMD)algorithm is used to extract the characteristics of the vibration signal of the rolling bearing.Moreover,the eigenmode function is obtained and then filtered by the kurtosis criterion.Consequently,the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained.Then the fault analysis is performed.To enhance the accuracy of fault diagnosis,based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing,four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers. 展开更多
关键词 Wind turbine rolling bearing fault diagnosis empirical mode decomposition
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Fault Feature Extraction of Rolling Bearing Based on an Improved Cyclical Spectrum Density Method 被引量:1
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作者 LI Min YANG Jianhong WANG Xiaojing 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第6期1240-1247,共8页
The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recogn... The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings. 展开更多
关键词 CYCLOSTATIONARY cyclical spectrum density rolling bearing fault diagnosis
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An improved resampling algorithm for rolling element bearing fault diagnosis under variable rotational speeds
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作者 赵德尊 李建勇 +1 位作者 程卫东 温伟刚 《Journal of Southeast University(English Edition)》 EI CAS 2017年第2期150-158,共9页
In order to address the issues of traditional resampling algorithms involving computational accuracy and efficiency in rolling element bearing fault diagnosis, an equal division impulse-based(EDI-based) resampling a... In order to address the issues of traditional resampling algorithms involving computational accuracy and efficiency in rolling element bearing fault diagnosis, an equal division impulse-based(EDI-based) resampling algorithm is proposed. First, the time marks of every rising edge of the rotating speed pulse and the corresponding amplitudes of faulty bearing vibration signal are determined. Then, every adjacent the rotating pulse is divided equally, and the time marks in every adjacent rotating speed pulses and the corresponding amplitudes of vibration signal are obtained by the interpolation algorithm. Finally, all the time marks and the corresponding amplitudes of vibration signal are arranged and the time marks are transformed into the angle domain to obtain the resampling signal. Speed-up and speed-down faulty bearing signals are employed to verify the validity of the proposed method, and experimental results show that the proposed method is effective for diagnosing faulty bearings. Furthermore, the traditional order tracking techniques are applied to the experimental bearing signals, and the results show that the proposed method produces higher accurate outcomes in less computation time. 展开更多
关键词 rolling element bearing fault diagnosis variable rotational speed equal division impulse-based resampling
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An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy 被引量:6
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作者 Zhiwu Shang Wanxiang Li +2 位作者 Maosheng Gao Xia Liu Yan Yu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2021年第4期121-136,共16页
For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intell... For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification capability.This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy.First,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features.Finally,the advantage of the deep belief network probability model is used as the fault classifier to identify the faults.The effectiveness of the proposed method was verified by a gearbox test-bed.Experimental results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy. 展开更多
关键词 fault diagnosis feature fusion information entropy Deep autoencoder Deep belief network
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Improved Multi-Bandwidth Mode Manifold for Enhanced Bearing Fault Diagnosis 被引量:1
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作者 Guifu Du Tao Jiang +2 位作者 Jun Wang Xingxing Jiang Zhongkui Zhu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第1期179-191,共13页
Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units mul... Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment(LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method.Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD(RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor(Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability. 展开更多
关键词 Variational mode decomposition Manifold learning Natural nearest neighbor algorithm rolling bearing fault diagnosis Time-frequency signal decomposition
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A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network 被引量:3
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作者 兴成宏 Xu Fengtian +2 位作者 Yao Ziyun Li Haifeng Zhang Jinjie 《High Technology Letters》 EI CAS 2015年第4期422-428,共7页
A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating c... A method combining information entropy and radial basis function network is proposed for fault automatic diagnosis of reciprocating compressors.Aiming at the current situation that the accuracy rate of reciprocating compressor fault diagnosis which depends on manual work in engineering is very low,we apply information entropy evaluation to select the sensitive features and make clear the corresponding relationship of characteristic parameters and failures.This method could reduce the feature dimension.Then,a complete fault diagnosis architecture has been built combining with radial basis function network which has the fast and efficient characteristics.According to the test results using experimental and engineering data,it is observed that the proposed fault diagnosis method improves the accuracy of fault automatic diagnosis effectively and it could improve the practicability of the monitoring system. 展开更多
关键词 information entropy radial basis function network fault automatic diagnosis re-ciprocating compressor sensitive feature
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A Signal Based “W” Structural Elements for Multi-scale Mathematical Morphology Analysis and Application to Fault Diagnosis of Rolling Bearings of Wind Turbines 被引量:2
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作者 Qiang Li Yong-Sheng Qi +2 位作者 Xue-Jin Gao Yong-Ting Li Li-Qiang Liu 《International Journal of Automation and computing》 EI CSCD 2021年第6期993-1006,共14页
Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature ex... Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results. 展开更多
关键词 fault diagnosis structural element multi-scale mathematical morphology rolling bearing correlation analysis
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A novel compound data classification method and its application in fault diagnosis of rolling bearings 被引量:1
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作者 Anxin Sun Ying Che 《International Journal of Intelligent Computing and Cybernetics》 EI 2017年第1期80-90,共11页
Purpose-The purpose of this paper is to provide a fault diagnosis method for rolling bearings.Rolling bearings are widely used in industrial appliances,and their fault diagnosis is of great importance and has drawn mo... Purpose-The purpose of this paper is to provide a fault diagnosis method for rolling bearings.Rolling bearings are widely used in industrial appliances,and their fault diagnosis is of great importance and has drawn more and more attention.Based on the common failure mechanism of failure modes of rolling bearings,this paper proposes a novel compound data classification method based on the discrete wavelet transform and the support vector machine(SVM)and applies it in the fault diagnosis of rolling bearings.Design/methodology/approach-Vibration signal contains large quantity of information of bearing status and this paper uses various types of wavelet base functions to perform discrete wavelet transform of vibration and denoise.Feature vectors are constructed based on several time-domain indices of the denoised signal.SVM is then used to perform classification and fault diagnosis.Then the optimal wavelet base function is determined based on the diagnosis accuracy.Findings-Experiments of fault diagnosis of rolling bearings are carried out and wavelet functions in several wavelet families were tested.The results show that the SVM classifier with the db4 wavelet base function in the db wavelet family has the best fault diagnosis accuracy.Originality/value-This method provides a practical candidate for the fault diagnosis of rolling bearings in the industrial applications. 展开更多
关键词 Support vector machine fault diagnosis Data classification Discrete wavelet transform rolling bearing
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RMA-CNN:A Residual Mixed Domain Attention CNN for Bearings Fault Diagnosis and Its Time-Frequency Domain Interpretability 被引量:1
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作者 Dandan Peng Huan Wang +1 位作者 Wim Desmet Konstantinos Gryllias 《Journal of Dynamics, Monitoring and Diagnostics》 2023年第2期115-132,共18页
Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations.Convolutional neural networks(CNNs)have achieved significant breakthroughs in machinery fault diagnosis.However,complex and varyin... Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations.Convolutional neural networks(CNNs)have achieved significant breakthroughs in machinery fault diagnosis.However,complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets,making it more challenging for CNNs to learn discriminative features.Furthermore,CNNs are often considered“black boxes”and lack sufficient interpretability in the fault diagnosis field.To address these issues,this paper introduces a residual mixed domain attention CNN method,referred to as RMA-CNN.This method comprises multiple residual mixed domain attention modules(RMAMs),each employing one attention mechanism to emphasize meaningful features in both time and channel domains.This significantly enhances the network’s ability to learn fault-related features.Moreover,we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications.Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks. 展开更多
关键词 attention interpretability CNN fault diagnosis rolling element bearings
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Fault diagnosis method of AC motor rolling bearing based on heterogeneous data fusion of current and infrared image
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作者 LIU Peijin GUO Zichen +2 位作者 HE Lin YAN Dongyang ZHANG Xiangrui 《Journal of Measurement Science and Instrumentation》 CAS 2024年第4期558-570,共13页
In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data... In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images.Firstly,VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal.On this basis,the current signal was transformed into a two-dimensional graph suitable for convolutional neural network,and the data set was classified by convolutional neural network and softmax classifier.Secondly,the infrared image was segmented and the fault features were extracted,so as to calculate the similarity with the infrared image of the fault bearing in the library,and further the sigmod classifier was used to classify the data.Finally,a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight,and the motor bearing fault diagnosis result was obtained.Through experimental verification,the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation,and the accuracy of fault diagnosis can reach 98.85%. 展开更多
关键词 current signal infrared image decision level fusion rolling bearing fault diagnosis
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PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING 被引量:7
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作者 ZHANG Xinming HE Yongyong HAO Rujiang CHU Fulei 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2007年第2期104-108,共5页
Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of ... Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm, an optimization strategy for the waveform parameters of the mother wavelet is proposed with wavelet entropy as the optimization target. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT. 展开更多
关键词 rolling bearing fault diagnosis Acoustic emission (AE) Continuous wavelet transform (CWT) Genetic algorithm
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An adaptive morphological impulses extraction method and its application to fault diagnosis
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作者 He Wei Jiang Zhinong Gao Jinji Wang Hui 《High Technology Letters》 EI CAS 2010年第3期318-323,共6页
An adaptive morphological impulses extraction method (AMIE) for bearing fault diagnosis is pro- posed. This method uses the morphological closing operation with a flat structuring element (SE) to extract impulsive... An adaptive morphological impulses extraction method (AMIE) for bearing fault diagnosis is pro- posed. This method uses the morphological closing operation with a flat structuring element (SE) to extract impulsive features from vibration signals with strong background noise. To optimize the flat SE, firstly, a theoretical study is carried out to investigate the effects of the length of the flat SE. Then, based on the theoretical findings, an adaptive algorithm for the flat SE optimization is proposed. The AMIE method is tested by the simulated signal and bearing vibration signals. The test results show that this method is effective and robust in extracting impulsive features. 展开更多
关键词 impulsive feature structuring element optimization rolling bearing fault diagnosis
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