Awesome-Graph-Neural-Network-for-PHM

Early access articles, Journals, and Conferences

Graph neural network (GNN) based Fault Diagnosis

Early Access Articles

• T. Li, Z. Zhao, C. Sun, R. Yan and X. Chen, "Multi-receptive Field Graph Convolutional Networks for Machine Fault Diagnosis," in IEEE Transactions on Industrial Electronics. doi: 10.1109/TIE.2020.3040669 Abstract: Deep learning (DL) based methods have swept the field of mechanical fault diagnosis, because of the powerful ability of feature representation. However, many of existing DL methods fail in relationship mining between signals explicitly. Unlike those deep neural networks, graph convolutional networks (GCNs) taking graph data with topological structure as input is more efficient for data relationship mining, making GCN to be powerful for feature representation from graph data in non-Euclidean space. Nevertheless, existing GCNs have two limitations. Firstly, most GCNs are constructed on unweighted graphs, considering importance of neighbors as the same, which is not in line with reality. Secondly, the receptive field of GCNs is fixed, which limits the effectiveness of GCNs for feature representation. To address these issues, a multi-receptive field graph convolutional network (MRF-GCN) is proposed for effective intelligent fault diagnosis. In MRF-GCN, data samples are converted into weighted graphs to indicate differences in relationship of data samples. Moreover, MRF-GCN learns not only features from different receptive field, but also fuses learned features as an enhanced feature representation. To verify the efficacy of MRF-GCN for machine fault diagnosis, case studies are implemented, and the results show that MRF-GCN can achieve superior performance even under imbalanced dataset. keywords: {Deep learning;graph convolutional networks;multi-receptive field;mechanical fault diagnosis}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9280401&isnumber=4387790

• T. Li, Z. Zhao, C. Sun, R. Yan and X. Chen, "Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions," in IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2021.3075016 Abstract: Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, domain label, and data structure, are essential to bridging the labeled source domain and unlabeled target domain. However, most existing UDA-based methods use only the former two information and ignore the modeling of data structure, which make the information contained in the features extracted by the deep network incomplete. To tackle this issue, a domain adversarial graph convolutional network (DAGCN) is proposed to model the three types of information in a unified deep network and achieving unsupervised domain adaptation. The first two types of information are model by the classifier and domain discriminator, respectively. In data structure modeling, a convolutional neural network (CNN) is firstly employed to exact features from input signals. After that, the CNN features are input to the proposed graph generation layer to construct instance graphs by mining the relationship of structural characteristics of samples. Then, the instance graphs are modeled by a graph convolutional network, and the Maximum Mean Discrepancy metric is leveraged to estimate the structure discrepancy of instance graphs from different domains. Experimental results conducted on two case studies demonstrate that the proposed DAGCN can not only obtain the best performance among the comparison methods, but also can extract transferable features for domain adaptation. The code library is available at: https://github.com/HazeDT/DAGCN. keywords: {Feature extraction;Fault diagnosis;Convolution;Data structures;Data models;Adaptation models;Data mining;Fault diagnosis;unsupervised domain adaptation;graph convolutional network;variable working conditions}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9410617&isnumber=4407674

• Z. Chen, J. Xu, T. Peng and C. Yang, "Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge," in IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2021.3059002 Abstract: Deep-neural network-based fault diagnosis methods have been widely used according to the state of the art. However, a few of them consider the prior knowledge of the system of interest, which is beneficial for fault diagnosis. To this end, a new fault diagnosis method based on the graph convolutional network (GCN) using a hybrid of the available measurement and the prior knowledge is proposed. Specifically, this method first uses the structural analysis (SA) method to prediagnose the fault and then converts the prediagnosis results into the association graph. Then, the graph and measurements are sent into the GCN model, in which a weight coefficient is introduced to adjust the influence of measurements and the prior knowledge. In this method, the graph structure of GCN is used as a joint point to connect SA based on the model and GCN based on data. In order to verify the effectiveness of the proposed method, an experiment is carried out. The results show that the proposed method, which combines the advantages of both SA and GCN, has better diagnosis results than the existing methods based on common evaluation indicators. keywords: {Fault diagnosis;Feature extraction;Knowledge engineering;Neural networks;Convolution;Matrix decomposition;Fourier transforms;Deep neural network;fault diagnosis;graph convolutional network (GCN);prior knowledge;structural analysis (SA)}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9377671&isnumber=6352949

• X. Zhao, M. Jia and Z. Liu, "Semi-Supervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System with Limited Labeled Data," in IEEE Transactions on Industrial Informatics. doi: 10.1109/TII.2020.3034189 Abstract: The labeled monitoring data collected from the electromechanical system is limited in the real industries, traditional intelligent fault diagnosis methods cannot achieve satisfactory accurately diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semi-supervised graph convolution deep belief network (SSGCDBN) algorithm is proposed in this study. Specifically, the labeled and unlabeled samples are firstly employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss (CL). Finally, the local and discriminative objective functions are reconstructed under the semi-supervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66% accuracy with only 10% of training labeled data, which indicates that it is a promising semi-supervised intelligent fault diagnosis method. keywords: {Fault diagnosis;Semisupervised learning;Informatics;Electromechanical systems;Deep learning;Convolution;Industries;Elector-mechanical system;fault diagnosis;semi-supervised learning;convolution deep belief network (CDBN);graph neural network (GNN)}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9244619&isnumber=4389054

Journals

• X. Yu, B. Tang and K. Zhang, "Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-14, 2021, Art no. 6502714. doi: 10.1109/TIM.2020.3048799 Abstract: The fault diagnosis of the gearbox of wind turbines is a crucial task for wind turbine operation and maintenance. Although a convolutional neural network can extract the related information of adjacent sampling points using kernels, traditional deep learning methods have not leveraged related information from points with a large span of vibration signal data. In this article, a novel fast deep graph convolutional network is proposed to diagnose faults in the gearbox of wind turbines. First, the original vibration signals of the wind turbine gearbox are decomposed by wavelet packet, which presents time-frequency features as graphs. Then, graph convolutional networks are introduced to extract the features of points with a large span of the defined graph samples. Finally, the fast graph convolutional kernel and the particular pooling improvement are used to reduce the number of nodes and achieve fast classification. Experiments on two data sets are performed to verify the efficacy of the proposed method. keywords: {Convolution;Wind turbines;Feature extraction;Wavelet packets;Fault diagnosis;Vibrations;Kernel;Fast deep graph convolutional networks (FDGCNs);fault diagnosis;wavelet packet transform (WPT);wind turbine gearbox}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9312649&isnumber=9259274

• S. Li et al., "A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks," in IEEE Access, vol. 8, pp. 92743-92753, 2020. doi: 10.1109/ACCESS.2020.2995198 Abstract: Bearing fault diagnosis has attracted increasing attention due to its importance in the health status of rotating machinery. The data-driven models based on deep learning (DL) have become more and more intelligent in the field of fault diagnosis, and among them convolutional neural network (CNN) has been widely used in recent researches. However, traditional CNN is not easy to capture right fault features due to their fixed geometric structures, especially under complex working conditions in fault diagnosis. To address these challenges, we propose a novel model by combining InceptionResnetV2 with Deformable Convolution Networks, named DeIN. We replace the basic form of convolution with deformable convolution in specific layers, and a main classifier and an auxiliary classifier are designed to output the classification result of our proposed model, to adapt to the non-rigid characters and larger receptive field in time-frequency graph (TFG). Experimentally, the one-dimensional signals are transformed into TFGs and as input of the proposed model, and this aims to find useful features during the training process. To verify the generalization ability of the proposed model, we apply a set of cross-over tests based on two popular datasets, and our model achieved 99.87% and 94.52% highest-precision fault classification results comparing with other state-of-the-art CNN models. keywords: {Convolution;Kernel;Fault diagnosis;Feature extraction;Data models;Employee welfare;Time-frequency analysis;Rolling element bearing;fault diagnosis;InceptionResnetV2;deformable convolution;time-frequency graph}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9094580&isnumber=8948470

• W. Liao, D. Yang, Y. Wang and X. Ren, "Fault diagnosis of power transformers using graph convolutional network," in CSEE Journal of Power and Energy Systems, vol. 7, no. 2, pp. 241-249, March 2021. doi: 10.17775/CSEEJPES.2020.04120 Abstract: Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy. keywords: {Fault diagnosis;Oil insulation;Measurement;Power transformer insulation;Feature extraction;Deep learning;Social networking (online);Power transformer;fault diagnosis;graph con-volutional network;similarity metrics}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9299500&isnumber=9384489

• Y. Wang, M. Yang, Y. Li, Z. Xu, J. Wang and X. Fang, "A Multi-Input and Multi-Task Convolutional Neural Network for Fault Diagnosis Based on Bearing Vibration Signal," in IEEE Sensors Journal, vol. 21, no. 9, pp. 10946-10956, 1 May1, 2021. doi: 10.1109/JSEN.2021.3061595 Abstract: Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery. Existing methods use signals from a single dimension, limiting diagnostic generality and accuracy. To address these limitations and make improved use of signal features from multiple dimensions, a novel convolutional neural network model with multi-dimensional signal inputs and multi-dimensional task outputs called MIMTNet is proposed. First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. Then, the time domain signals, the frequency domain signals, and the time frequency graph are fed into the model and a special aggregation is performed after extracting features from the three corresponding branches. Finally, the outputs of the three-dimensional tasks are acquired by different full connection layers to process the aggregated features of bearing position, damage location within the bearing, and the damage size. Two common bearing vibration signal datasets are used to verify the generalization ability of our proposed method. And experimental results show that the proposed method effectively improves the bearing diagnosis capability of the deep learning model. keywords: {Feature extraction;Convolution;Fault diagnosis;Continuous wavelet transforms;Sensors;Task analysis;Deep learning;Intelligent fault diagnosis;convolutional neural network;multi-input;multi-task;ghost module;feature aggregation}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9360815&isnumber=9395101

• X. Zhao, M. Jia, J. Bin, T. Wang and Z. Liu, "Multiple-Order Graphical Deep Extreme Learning Machine for Unsupervised Fault Diagnosis of Rolling Bearing," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-12, 2021, Art no. 3506012. doi: 10.1109/TIM.2020.3041087 Abstract: The intelligent fault diagnosis powered deep learning (DL) is widely applied in various practical industries, but the conventional intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive unlabeled industrial data. Thus, a new Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is proposed in this study. Specifically, the developed MGDELM algorithm mainly contains two parts: 1) one is unsupervised multiple-order feature extraction, the first-order proximity with Cauchy graph embedded is applied to extract the local structural information, and the second-order proximity is simultaneously employed for mining global structural information and 2) the other used is the unsupervised Fuzzy-C-Mean (FCM) into fault clustering built on the extracted multiple-order graph embedding features. Empirically, two cases of rolling bearing failure data validate the effectiveness of the proposed algorithm and fault diagnosis method. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. This study also provides a novel promising approach for UFD. keywords: {Fault diagnosis;Rolling bearings;Clustering algorithms;Feature extraction;Manifolds;Vibrations;Task analysis;Graph deep extreme learning machine (GDELM);graph neural network (GNN);local and global information;rolling bearing;unsupervised fault diagnosis (UFD)}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9290051&isnumber=9259274

• C. Sun, X. Chen, R. Yan and R. X. Gao, "Composite-Graph-Based Sparse Subspace Clustering for Machine Fault Diagnosis," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 5, pp. 1850-1859, May 2020. doi: 10.1109/TIM.2019.2923829 Abstract: Sparse subspace clustering (SSC) is an effective method to cluster sensing signal for fault diagnosis in mechanical systems. SSC is based on a global expression strategy describing each data point by other data points from all the potential clusters. A drawback of this strategy is that it generates nonzero elements in the nondiagonal blocks of the similarity matrix, thereby reducing the performance of the matrix for data discrimination. To improve SSC's performance, a composite-graph-based SSC (CG-SSC) method is developed by introducing distance among the data points into SSC, where the L1-norm of the sparse coefficients is replaced by the product of the data distance and the sparse coefficients. The distance serves as a constraint to the amplitude of the sparse coefficients. A sparse coefficient with small amplitude is assigned if the distance between the expressed data and the expressing data is large, and vice versa. This method supports the construction of a compact cluster. The effectiveness of the presented method is experimentally verified by data measured on several bearings and gearboxes with different types of faults. The method is also compared with classical clustering methods, and the results indicate its advantage for data sorting and clustering. keywords: {Fault diagnosis;Sparse matrices;Linear programming;Sensors;Vibrations;Artificial neural networks;Clustering methods;Blocky-diagonal constraint;composite graph;machine fault diagnosis;sparse subspace clustering (SSC)}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8740970&isnumber=9058729

Conferences:

• J. He and H. Zhao, "Fault Diagnosis and Location based on Graph Neural Network in Telecom Networks," 2020 International Conference on Networking and Network Applications (NaNA), Haikou City, China, 2020, pp. 304-309. doi: 10.1109/NaNA51271.2020.00059 Abstract: As the scale of telecom networks continues to expand and the structure becomes more complex, telecom networks generate a large number of continuous warning messages every day. It may lead to massive network paralysis. To solve this problem, this paper proposes a fault diagnosis scheme of telecom networks based on Graph Neural Network (GNN). The scheme can be applied to locate the fault-root-devices. Firstly, Long Short-Term Memory (LSTM) is used to cluster the state level of partial devices, and the results provide prior information for the construction of the GNN. Then, the embedded representation of graph network is introduced to aggregate the information of the whole graph. Finally, the device network is set up and the data information is embedded into the whole graph network. GNN model is built to diagnose the fault of all devices and locate the fault- root-devices. Moreover, the performance of the model constructed respectively by Graph Convolutional Network (GCN) and Graph Attention Network (GAT) are compared and analyzed. Based on public fault data, the experimental results show that the accuracy of GAT is higher than that of GCN; the correct rate of fault diagnosis can exceed 93 %; and the fault- root-devices can be accurately located. In addition, the complexity is only O(n) in our scheme. keywords: {Fault diagnosis;Performance evaluation;Analytical models;Graph neural networks;Telecommunications;Complexity theory;Paralysis;Telecom network;fault diagnosis;cluster analysis;Graph Neural Network;root cause analysis}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9353791&isnumber=9353745

• C. Li, L. Mo and R. Yan, "Rolling Bearing Fault Diagnosis Based on Horizontal Visibility Graph and Graph Neural Networks," 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), Xi'an, China, 2020, pp. 275-279. doi: 10.1109/ICSMD50554.2020.9261687 Abstract: The automatic extraction and learning features relying on artificial intelligence algorithms replace traditional manual features. More effective feature expression improves the performance of machine fault diagnosis with fewer requirements for labor and expertise. However, the present models only can process the data in Euclidean space. The relations between data points are ignored for a long time, which can play a significant role in distinguishing diverse faults patterns. To combat this issue, a novel model for bearing faults diagnosis is proposed by incorporating the horizontal visibility graph (HVG) and graph neural networks (GNN). In the proposed model, time series is converted to graph retaining invariant dynamic characteristics through the HVG algorithm, and the generated graphs are fed into a designed GNN model for feature learning and faults classification further. Finally, the proposed model is tested on two actual bearing datasets, and it shows state-of-the-art performance in the bearing faults diagnosis. The experimental results demonstrate that extracting relation information using HVG benefits bearing faults diagnosis. keywords: {Fault diagnosis;Feature extraction;Time series analysis;Signal processing algorithms;Task analysis;Rolling bearings;Data models;rolling bearing;fault diagnosis;horizontal visibility graph (HVG);graph neural networks (GNN)}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9261687&isnumber=9261628

• Q. S. Geng, F. H. Wang and D. X. Zhou, "Mechanical Fault Diagnosis of Power Transformer by GFCC Time-frequency Map of Acoustic Signal and Convolutional Neural Network," 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, 2019, pp. 2106-2110. doi: 10.1109/iSPEC48194.2019.8975318 Abstract: To carefully describe the mechanical condition information from transformer acoustic signals and then identify its typical mechanical faults, the combination of gammatone filter cepstral coefficient (GFCC) time-frequency graph of acoustic signals and Convolution Neural Network is proposed in this paper when considered the excellent sound recognition ability of human auditory system. The gammatone filterbank is first used to decompose the acoustic signals frequency of power transformer with the abundant samples of GFCC time-frequency diagram obtained. Then the intrinsic features of transformer acoustic signal GFCC time-frequency diagram samples are extracted by AlexNet convolution neural network, which is used as input of classifier for recognition. The calculated results of acoustic signals under normal and typical mechanical faults of a 10 kV dry-type transformer have shown that the proposed mechanical fault model of transformer has good recognition effect with mechanical fault model of transformer. The accuracy can reach more than 98%. The research results can provide an important basis for sound diagnosis of typical mechanical faults of power transformers. keywords: {power transformer;acoustic signal;gammatone filter cepstral coefficient;convolution neural network}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8975318&isnumber=8974850

• Y. Liu, Q. Pan, H. Wang and T. He, "Fault diagnosis of satellite flywheel bearing based on convolutional neural network," 2019 Prognostics and System Health Management Conference (PHM-Qingdao), Qingdao, China, 2019, pp. 1-6. doi: 10.1109/PHM-Qingdao46334.2019.8942957 Abstract: The bearing is one of the core components of the flywheel, providing a stable slewing support for the flywheel, and its operating state often directly affects the flywheel and even the entire spacecraft's normal operation. In view of the problem of automatic and accurate identification of the bearing faults, this paper uses convolutional neural network (CNN) to develop a satellite flywheel bearing fault intelligent diagnosis method. First, the vibration signal characteristics of satellite flywheel bearing under different faults are studied. Second, the time-domain signal graphs are constructed by combining vibration signals under multiple rotational speeds and used as feature input maps. Finally, the bearing fault intelligent diagnosis method is presented based on the excellent image recognition characteristics of CNN and the constructed feature maps. The experimental verification shows that the proposed method can achieve better diagnostic results. keywords: {Convolution;Fault diagnosis;Feature extraction;Convolutional neural networks;Flywheels;Time-domain analysis;Vibrations;satellite flywheel bearing;CNN;fault diagnosis;Multi-information fusion}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8942957&isnumber=8942805

• C. Fan, K. Liu and Q. Gao, "Fault diagnosis and isolation in complex dynamical networks with uncertainties," 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China, 2018, pp. 6072-6077. doi: 10.1109/CCDC.2018.8408196 Abstract: Fault diagnosis and isolation is necessary in obtaining reliable and stable operations of complex dynamical networks. This paper presents a novel fault detection and isolation (FDI) method for complex dynamic networks with uncertainties. In the proposed FDI architecture, a fault detection estimator (FDE) and a bank of fault isolation estimators (FIEs) are designed for fault diagnosis and isolation in each local subsystem. When a fault is detected by the FDE in one subsystem, the corresponding FIEs are activated to figure out which type of fault is taking place in the subsystem. It is effective to monitor changes in complex dynamical networks with uncertainties for fault diagnosis and isolation. In addition, some numerical simulations are done to illustrate the effectiveness of this method. keywords: {Fault diagnosis;Uncertainty;Fault detection;Observers;Symmetric matrices;Artificial neural networks;Estimation error;complex dynamical network;fault diagnosis;fault isolation;uncertainties}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8408196&isnumber=8407034

• H. Wang, J. Xu and R. Yan, "Bearing Fault Diagnosis Based on Visual Symmetrized Dot Pattern and CNNs," 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand, 2019, pp. 1-6. doi: 10.1109/I2MTC.2019.8827101 Abstract: This paper presents a new bearing fault diagnostic method based on symmetrized dot pattern (SDP) and convolutional neural networks (CNNs). Firstly, a time-domain vibration signal is directly transformed into a snowflake image in the polar coordinate to visualize fault by using SDP technique, and the sample library of visual SDP graphs of each running state is established. Then, shape difference features of SDP images are automatically extracted by the designed CNNs model to form a feature vector. Finally, the formed feature vector is used as the input to a Softmax classifier for recognizing the bearing fault state. Relative to the fault visualization of time-frequency analysis methods, the snowflake image of bearing vibration signal is directly acquireded by SDP technique without Fourier transforms, which is simpler with better performance. Experimental results show that the proposed method using SDP and CNNs can not only accurately recognize the bearing states, but also identify the relative position that fault occurred. The proposed method is more applicable for intelligent fault diagnosis of rolling bearing with 100% diagnosis accuracy. keywords: {Feature extraction;Fault diagnosis;Visualization;Convolution;Vibrations;Time-frequency analysis;Rolling bearings;bearing;convolutional neural networks;fault diagnosis;fault visualization;symmetrized dot pattern}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8827101&isnumber=8826808

• J. Shang and T. R. Lin, "Varying Speed Bearing Fault Diagnosis Based on Synchroextracting Transform and Deep Residual Network," 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, BC, Canada, 2020, pp. 1-5. doi: 10.1109/APARM49247.2020.9209580 Abstract: An intelligent fault diagnosis method is proposed in this study based on Synchroextracting Transform (SET) and deep residual network (DRN) for fault diagnosis of rolling element bearings operating under varying speed condition. Firstly, the bearing condition monitoring (CM) data is processed using SET to obtain the time frequency spectrum graphs as the feature set. The feature set is then used as the input features to train the DRN model. Finally, the trained DRN model is used for an automated bearing fault diagnosis. The classification results show that the proposed method can achieve high recognition accuracy for rolling bearings operating under varying speed conditions. keywords: {Fault diagnosis;Time-frequency analysis;Training;Maintenance engineering;Classification algorithms;Transforms;rolling element bearing;varying speed condition;synchroextracting transform;deep residual network;fault diagnosis}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9209580&isnumber=9209323

• X. Zhao, Z. Liu, T. Wang, J. Bin and M. Jia, "Unsupervised Fault Diagnosis of Machine via Multiple-Order Graphical Deep Extreme Learning Machine," 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, BC, Canada, 2020, pp. 1-6. doi: 10.1109/APARM49247.2020.9209447 Abstract: The intelligent fault diagnosis powered deep learning (DL) is widely applied in various real industrial fields. However, the classical intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive industrial data. At the same time, the scarcity of the labeled information can also result in inferior generalization performance. To this end, a new multiple-order graphical deep extreme learning machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is designed in this study. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. Empirically, rolling bearing failure data validates the effectiveness of the designed algorithm and fault diagnosis method. keywords: {Fault diagnosis;Feature extraction;Maintenance engineering;Rolling bearings;Manifolds;Signal processing algorithms;Rolling bearing;Unsupervised fault diagnosis (UFD);Multiple-Order Graphical Deep Extreme Learning Machine (MGDELM);Graph Neural Network (GNN);Local and global information}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9209447&isnumber=9209323