Rolling Bearing Fault Diagnosis Based on Horizontal Visibility Graph and Graph Neural Networks

Li, Chenyang; Mo, Lingfei; Yan, Ruqiang

doi:10.1109/icsmd50554.2020.9261687

Cited by 30 publications

(9 citation statements)

References 15 publications

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“…Compared to pure numerical information, the proposed method gives additional valuable information for classification. Li et al (2020a) prove that the GNN model outperforms the RNN model for bearing faults diagnosis.…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 79%

“…By aggregating information from the node's neighbors at any depth, GNN can more effectively extract and inference data relationships. Using a horizontal visibility graph (HVG) and a GNN, Li et al (2020a) suggested a new model for bearing faults diagnosis. The HVG algorithm turns a time series sample into a graph with condition-specific topology.…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

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Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

et al. 2022

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Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.

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Section: Graph Neural Network (Gnn)mentioning

confidence: 79%

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

et al. 2022

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“…In fault diagnosis applications, the interpretation of nodes and edges varies. For instance, Li et al [30] treated each data point in a sample as a node, using the data point's value as the node embedding and connecting nodes through HVG. Miao et al [10] considered signals from each sensor in a robot as nodes, with signal values as embeddings, and established connections based on kinematic equations.…”

Section: Graph Convolutionmentioning

confidence: 99%

Transfer Learning for Bearing Fault Diagnosis based on Graph Neural Network with Dilated KNN and Adversarial Discriminative Domain Adaptation

Tang,

Liu,

Qiu

et al. 2024

Meas. Sci. Technol.

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Abstract. Graph neural networks (GNNs) have emerged as a forefront in deeplearning, notably influencing research in mechanical fault diagnosis. Transfer learning(TL), particularly through domain adaptation (DA) techniques, has found applicationin machinery fault diagnosis by training models under one working condition anddeploying them under another. While efforts have been made to integrate GNNswith DA techniques to alleviate data distribution discrepancies by investigating theinter-sample relationships, challenges persist: reliance on K-Nearest Neighbor (KNN)for graph generation emphasizes close relationships, neglecting distant ones; batchprocessing limits real-time fault diagnosis; and transfer between different-sized bearingsis nearly unexplored. To address these limitations, a novel framework for GNN-based domain adaptation in machinery fault diagnosis is proposed. Initially, aconvolutional neural network (CNN) extracts node embeddings from the ContinuousWavelet Transform (CWT) graph of raw vibration signals. Subsequently, a graphgeneration layer (GGL) based on dilated KNN captures both close and distant samplerelationships, addressing the long-range dependency issue. Two GNN blocks are thenapplied for inter-sample relationships investigation and further feature extraction withthe outputs directed to a linear classifier during source domain pretraining. Followingpretraining, adversarial discriminative domain adaptation (ADDA) is leveraged tomitigate domain distribution discrepancies. Additionally, a novel graph constructionmethod that combines existing training samples with a new single sample is proposed,enabling fault prediction with single instances for real-time online fault diagnosis.Evaluation on datasets with varying working conditions and bearings of different sizesdemonstrates the superior performance of our method to other comparison methods.

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“…To further demonstrate the superiority of our proposed approach, we compared it with other state-of-the-art methods, as shown in table 3. The deep learning methods considered include GCN-based, GATbased, and GIN-based methods denoted as method 1 (GCN proposed in [15]), method 2 (GAT proposed in [31]), method 3 (HVG-GIN proposed in [35]), and method 4 (WHVG-GIN+ proposed in [34]). Notably, our approach achieved remarkable results, with accuracy, precision, recall, and F1-score values reaching 100.00% across the entire test set.…”

Section: Comparison With Other Modelsmentioning

confidence: 99%

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Liu,

2024

Meas. Sci. Technol.

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Abstract—Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph modeling and the M2SGCN network, incorporating statistical fusion. Specifically, we propose a novel dynamic multiscale graph (DMG) modeling method to derive visibility graph (VG) data and horizontal visibility graph (HVG) data from vibration signals across multiple scales. Next, we establish M2SGCN, a comprehensive neural network architecture that learns global and local features simultaneously, providing a more precise representation. Finally, we utilize a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) to integrate primary decision results for enhanced fault diagnosis accuracy. Furthermore, we analyze two datasets obtained from single-phase and three-phase power transformers to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1-scores, the proposed method excels in multiple aspects, successfully detecting fault states prior to their occurrence and achieving outstanding performance.

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Rolling Bearing Fault Diagnosis Based on Horizontal Visibility Graph and Graph Neural Networks

Cited by 30 publications

References 15 publications

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Transfer Learning for Bearing Fault Diagnosis based on Graph Neural Network with Dilated KNN and Adversarial Discriminative Domain Adaptation

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

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