检索结果-南通市图书馆

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 932-945页

作者： Bin Yang yaguo lei Xiang Li Naipeng Li Asoke K.Nandi the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System the School of Mechanical EngineeringXi’an Jiaotong UniveristyXi’an 710049 the Hunan Provincial Key Labortory of Health Maintenance for Mechanical Equipment Hunan Univeristy of Science and TechnologyXiangtan 411201China the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniveristyXi’an 710049China the Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source ***,in engineering scenarios,achieving such high-quality label annotation is difficult and *** incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled *** overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three ***,a residual network with dual classifiers is to learn features from cross-domain ***,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source *** the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised ***,the adaptation trajectories are designed for sample distributions across *** ensures that the target domain samples are only adapted with the pure-labeled *** LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related *** results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

关键词： Deep transfer learning domain adaptation incorrect label annotation intelligent fault diagnosis rotating machines

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 788-790页

作者： Xinrui Chen Xiang Li Shupeng Yu yaguo lei Naipeng Li Bin Yang the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic *** event-based camera is adopted to capture the machine vibration states in the perspe... 详细信息

关键词： fault less diagnosis

Attribute-driven Fuzzy Fault Tree Model for Adaptive Lubricant Failure Diagnosis

在线全文

学校读者我要写书评

暂无评论

Journal of Dynamics, Monitoring and Diagnostics 2024年第3期3卷 207-215页

作者： Shuo Wang Yishi Chang Tonghai Wu Zhidong Han yaguo lei Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an710049ShaanxiChina

Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in ***,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring *** address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and ***,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant *** particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple *** this model,the lubricant conditions can be accurately assessed through rule-based ***,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring *** verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual *** experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing *** for the engine lubricant test,a high accuracy of 90%was achieved.

关键词： lubricant failure diagnosis fuzzy fault tree attribute guidance rule reasoning

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Flexible Time Domain Averaging Technique

自动化学报（英文版） 2024年第10期 2068页

作者： Xiang Li Shupeng Yu yaguo lei Naipeng Li Bin Yang

来源：博看期刊评论

在线全文

博看期刊

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2013年第5期26卷 1022-1030页

作者： ZHAO Ming LIN Jing lei yaguo WANG Xiufeng School of Mechanical Engineering Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Shaanxi Key Laboratory of Product Quality Assurance & Diagnosis

Time domain averaging(TDA) is essentially a comb filter,it cannot extract the specified harmonics which may be caused by some faults,such as gear ***,TDA always suffers from period cutting error(PCE) to different *** improved TDA methods have been proposed,however they cannot completely eliminate the waveform reconstruction error caused by *** order to overcome the shortcomings of conventional methods,a flexible time domain averaging(FTDA) technique is established,which adapts to the analyzed signal through adjusting each harmonic of the comb *** this technique,the explicit form of FTDA is first constructed by frequency domain ***,chirp Z-transform(CZT) is employed in the algorithm of FTDA,which can improve the calculating efficiency *** the signal is reconstructed in the continuous time domain,there is no PCE in the *** validate the effectiveness of FTDA in the signal de-noising,interpolation and harmonic reconstruction,a simulated multi-components periodic signal that corrupted by noise is processed by *** simulation results show that the FTDA is capable of recovering the periodic components from the background noise ***,it can improve the signal-to-noise ratio by 7.9 dB compared with conventional *** are also carried out on gearbox test rigs with chipped tooth and eccentricity gear,*** is shown that the FTDA can identify the direction and severity of the eccentricity gear,and further enhances the amplitudes of impulses by 35%.The proposed technique not only solves the problem of PCE,but also provides a useful tool for the fault symptom extraction of rotating machinery.

关键词： time domain averaging chirp Z-transform period cutting error de-noising interpolation

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

在线全文

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 121-134页

作者： Xiang Li Yixiao Xu Naipeng Li Bin Yang yaguo lei IEEE the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China Student Member Senior Member

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple ***,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating *** this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different *** learning is further introduced to extract generalized sensor-invariant *** explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor *** experimental results suggest the proposed approach is well suited for real industrial applications.

关键词： Adversarial training data fusion deep learning remaining useful life(RUL)prediction sensor malfunction

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

在线全文

学校读者我要写书评

暂无评论

机械工程前沿:英文版 2023年第2期18卷 267-281页

作者： Shuhui WANG yaguo lei Na LU Xiang LI Bin YANG Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China Systems Engineering Institute Xi’an Jiaotong UniversityXi’an 710049China

Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of *** the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis ***,existing studies suffer from two ***,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault ***,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is *** article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition ***,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore *** proposed method is demonstrated by experiments on an induction motor and a centrifugal *** show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition fault localization multi-sensor relations network analysis graph neural network

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

在线全文

学校读者我要写书评

暂无评论

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

自动化学报（英文版） 2024年第3期

作者： Xinrui Chen Xiang Li Shupeng Yu yaguo lei Naipeng Li Bin Yang

来源：博看期刊评论

在线全文

博看期刊

学校读者我要写书评

暂无评论

Journal of Dynamics, Monitoring and Diagnostics 2022年第1期1卷 2-8页

作者： Wenting Wang yaguo lei Tao Yan Naipeng Li Asoke KNandi Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’anChina Department of Electronic and Electrical Engineering Brunel University LondonUxbridgeUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)*** various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,have gained extensive attention for their ability to capture temporal *** existing RNN-based methods have demonstrated their RUL prediction effectiveness,they still suffer from the following two limitations:1)it is difficult for the RNN to directly extract degradation features from original monitoring data and 2)most RNN-based prognostics methods are unable to quantify RUL *** address the aforementioned limitations,this paper proposes a new prognostics method named residual convolution LSTM(RC-LSTM)*** the RC-LSTM,a new ResNet-based convolution LSTM(Res-ConvLSTM)layer is stacked with a convolution LSTM(ConvLSTM)layer to extract degradation representations from monitoring ***,under the assumption that the RUL follows a normal distribution,an appropriate output layer is constructed to quantify the uncertainty of prediction ***,the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests.

关键词： Deep learning residual convolution LSTM network remaining useful life prediction uncertainty quantification