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intelligent fault diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

Chinese Journal of Mechanical Engineering 2017年第6期30卷 1314-1324页

作者： Guo-Qian Jiang Ping Xie Xiao Wang Meng Chen Qun He School of Electrical Engineering Yanshan University The Six Branch of Qinhuangdao Port Co Ltd

The performance of traditional vibration based fault diagnosis methods greatly depends on those hand- crafted features extracted using signal processing algo- rithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised represen- tation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal struc- tures, this paper proposes a multiscale representation learning （MSRL） framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at dif- ferent scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multi- scale representations. Finally, the multiscale representa- tions are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies： motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

关键词： intelligent fault diagnosis Vibration signals Unsupervised feature learning Sparse filtering Multiscalefeature extraction

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A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds

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Journal of Central South University 2019年第6期26卷 1607-1618页

作者： ZHANG Zhong-wei CHEN Huai-hai LI Shun-ming WANG Jin-rui State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Modern agricultural mechanization has put forward higher requirements for the intelligent defect ***,the fault features are usually learned and classified under all speeds without considering the effects of speed *** overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this *** the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the *** proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect *** proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural *** fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.

关键词： intelligent fault diagnosis short time Fourier transform sparse filtering softmax regression

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intelligent fault diagnosis Research of Electromagnetic Interference Based on the Combination of CBR and RBR

Intelligent Fault Diagnosis Research of Electromagnetic Inte...

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第二十九届中国控制会议

作者： GANG Ming-Gang,CHEN Jie,YANG Bo,CAI Tao,CHENG Lan College of Automation Beijing Institute of Technology Beijing 100081,P.R.China.

It is difficult to diagnose the faults of electromagnetic interference（EMI）since the knowledge of it is *** order to solve the problem,an intelligent fault diagnosis method of electromagnetic interference is proposed based on the combination of CBR and *** the proposed method CBR is relied on mainly and supplemented with the RBR,i.e.,RBR is enabled when the similarity of the target case and the source case of CBR case base is below the threshold *** fault diagnosis problem of electromagnetic interference can be solved much better by the proposed method.

关键词： intelligent fault diagnosis CBR RBR EMI

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intelligent fault diagnosis for LVMS

Intelligent Fault Diagnosis for LVMS

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中国真空学会第六届全国会员代表大会暨学术会议

作者： DECHUN-Ba ,QING Wang ,XIAODONG-Wang (Vacuum and Fluid Engineering Research Center of Northeastern University,Shenyang 110004,China.

Large Vacuum Metallurgical System(LVMS) is complex-large system used to refine and purify *** practice the system often get out of order which effects product quality so far as to production *** most monitoring software of the system are not able to process state decision and fault *** fault occurs,workmen have to manually find position of fault and removal of *** it is inconvenience for *** special position of fault and failure cause are uneasy to be found *** it is important that an intelligent fault diagnosis system for LVMS is *** this paper species and features fault of LVMS is studies and the fault tree is found against the *** relationship amony faults is obtained,at the same time original information acquisition system is *** process conditions of LVMS demands for quick-diagnosis. The quick diagnosis means quick learning and *** this research decision tree theory is used to learn and classify reducts *** an intelligent fault diagnosis model based on rough sets-decision tree theory is put forward aiming at the high-duty vacuum metallurgical system of which the possible faults take place frequently and unwarnedly and need quick and exact fault diagnosis. Thus,effectiveness of the algorithm is proved through an analysis of the exemplification.A RH-KTB(LVMS) intelligent fault diagnosis system based on the model is developed.

关键词： Vacuum Metallurgical intelligent fault diagnosis fault tree Decision tree

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Integrated intelligent fault diagnosis Approach to TE Process

Integrated Intelligent Fault Diagnosis Approach to TE Proces...

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第二十九届中国控制会议

作者： YANG Qing 1,2,TIAN Feng 2,WU Dongsheng 1,2,WANG Dazhi 1,3 1.College of Optical and Electronical Information,Changchun University of Science and Technology,Changchun 130022,P.R.China2.School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,P.R.China 3.College of Information Science and Engineering,Northeast University,Shenyang 110004,P.R.China

An integrated algorithm based on lifting wavelets and probabilistic neural network（LWPNN）for classifying the industrial system faults was presented in this *** the data were preprocessed to remove noise by lifting scheme wavelets,which were faster than first generation wavelets,and then PNN was used to diagnose *** validate the performance and effectiveness of the proposed scheme,LWPNN was applied to diagnose the faults in TE *** studies showed that the proposed algorithm not only provided an accepted degree of accuracy in fault classification under different fault conditions,but also was reliable,fast and computationally efficient tool.

关键词： fault Detection and diagnosis LWPNN Lifting wavelets intelligent fault diagnosis TE process

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Label Recovery and Trajectory Designable Network for Transfer fault diagnosis of Machines With Incorrect Annotation

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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

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Intrinsic component filtering for fault diagnosis of rotating machinery

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Chinese Journal of Aeronautics 2021年第1期34卷 397-409页

作者： Zongzhen ZHANG Shunming LI Jiantao LU Yu XIN Huijie MA College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

fault diagnosis of rotating machinery has always drawn wide *** this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault *** can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault *** can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior *** and experimental signals of bearing fault are used to validate the performance of *** results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.

关键词： Compound fault separation intelligent fault diagnosis Intrinsic component filtering Unsupervised learning Weak signature detection

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A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

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Protection and Control of Modern Power Systems 2022年第1期7卷 324-337页

作者： Dongdong Li Yang Zhao Yao Zhao School of Electrical Engineering Shanghai University of Electric PowerNo.2103Pingliang RoadYangpu DistrictShanghai 200090China

The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and *** fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled ***,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and *** some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual *** a potential tool,transfer learning adapts a network trained in a source domain to its application in a target ***,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary *** the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault *** groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.

关键词： Wind turbine planetary gearbox Lumped-parameter dynamic model intelligent fault diagnosis Convolutional neural network Transfer learning theory

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The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

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International Journal of intelligent Computing and Cybernetics 2022年第3期15卷 401-413页

作者： Shanling Han Shoudong Zhang Yong Li Long Chen College of Mechanical and Electronic Engineering Shandong University of Science and TechnologyQingdaoChina College of Materials Science and Engineering Shandong University of Science and TechnologyQingdaoChina

Purpose-intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the *** present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification ***,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault *** improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault ***/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance ***,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify *** from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool ***-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the *** experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification ***,the model can intuitively classify faults with higher ***/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is *** multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.

关键词： intelligent fault diagnosis Bearing fault Multilabel classification CNN-GRU Binary relevance method

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A fuzzy neural network evolved by particle swarm optimization

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Journal of Harbin Institute of Technology(New Series) 2007年第3期14卷 316-321页

作者：彭志平彭宏 Dept.of Computer Science & Technology Maoming College School of Computer Science & Engineering South China University of Technology

A cooperative system of a fuzzy logic model and a fuzzy neural network(CSFLMFNN)is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the *** PSO-CSFLMFNN is constructed by introducing particle swarm optimization(PSO)into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural *** evolutionary fuzzy neural network implements accuracy fuzzy inference without rule ***-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment,in which the cooperative system is proved to be *** is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.

关键词： fuzzy neural network evolving particle swarm optimization intelligent fault diagnosis

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