Graph neural network-based fault diagnosis: a review

Chen, Zhiwen; Xu, Jiamin; Alippi, Cesare; Ding, Steven X.; Shardt, Yuri A.W.; Peng, Tao; Yang, Chunhua

doi:10.48550/arxiv.2111.08185

Cited by 11 publications

(12 citation statements)

References 52 publications

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“…Compared with CNNs, GNNs can process complex spatial data relationships. The graph convolutional operation mainly generates vertex representations through the aggregation of features from both the given vertex and its neighboring vertices [70]. GNNs aim to enable nodes within graphs to learn embeddings that contain information about their neighborhood.…”

Section: Discussionmentioning

confidence: 99%

Graph neural networks for image‐guided disease diagnosis: A review

et al. 2023

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Medical imaging is playing an increasingly crucial role in disease diagnosis. Numerous deep learning-based methods have been developed for imageguided automatic disease diagnosis. Most of the methods have harnessed conventional convolutional neural networks, which are directly applied in the regular image domain. However, some irregular spatial patterns revealed in medical images are also critical to disease diagnosis, since they can describe latent relations in different image regions of a subject (e.g., different focal lesions in an image) or between different groups (e.g., Alzheimer's disease and healthy control). Therefore, how to exploit and analyze irregular spatial patterns and their relations has become a research challenge in the field of imageguided disease diagnosis. To address this challenge, graph neural networks (GNNs) are proposed to perform the convolution operation on graphs. Graphs can naturally represent irregular spatial structures. Because of their ability to aggregate node features, edge features, and graph structure information to capture and learn hidden spatial patterns in irregular structures, GNN-based algorithms have achieved promising results in the detection of various diseases. In this paper, we introduce commonly used GNN-based algorithms and systematically review their applications to disease diagnosis. We summarize the workflow of GNN-based applications in disease diagnosis, ranging from localizing the regions of interest and edge construction to modeling. Furthermore, we discuss the limitations and outline potential research directions for GNNs in disease diagnosis.

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Section: Discussionmentioning

confidence: 99%

Graph neural networks for image‐guided disease diagnosis: A review

et al. 2023

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“…Although the above deep learning techniques can effectively capture the hidden features or model the inherent knowledge from input data in an end-to-end way, most of them ignore the inter-dependencies between data or various physical measurements of multiple sensors [ 140 ]. Since [ 141 ] first applied neural networks to directed acyclic graphs, graph neural networks (GNN) have successfully handled data characterized by complex spatiotemporal relationships [ 142 ]. Although deep learning effectively captures the hidden patterns in Euclidean domains, more data are generated from non-Euclidean domains and represented as graphs with complex spatiotemporal relationships among objects.…”

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

“…Owing to the capability to model relationships in data, GNN has been receiving attentions from researchers in the FDP community recently, and the challenges faced in FDP are the appropriate way of constructing and realizing the graph [ 142 ]. Figure 11 gives an example diagnosis pipeline based on GCN.…”

Section: Part Ii: Supervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

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Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

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“…To tackle this issue, the technological process flow is utilized to construct the graph [22], with the sensors being treated as nodes and interrelationships between system signals being captured through edge connections. For instance, Wu and Zhao directly connected all units in the system based on the process flow for fault diagnosis [1].…”

Section: Introductionmentioning

confidence: 99%

Spatial weighted graph-driven fault diagnosis of complex process industry considering technological process flow

Zhang,

Liu,

et al. 2023

Meas. Sci. Technol.

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Each chemical process industry system possesses unique process knowledge, which serves as a representation of the system’s state. As graph-theory based methods are capable of embedding process knowledge, they have become increasingly crucial in the field of process industry diagnosis. The fault representation ability of the diagnosis model is directly associated with the quality of the graph. Unfortunately, simple fully connected graphs fail to strengthen the internal connections within the same process but weaken the interactive connections between different processes. Moreover, each node in the graph is considered equally important, making it impossible to prioritize crucial system monitoring indicators. To address the above shortcomings, this paper presents a spatial weighted graph (SWG)-driven fault diagnosis method of complex process industry considering technological process flow. Initially, the physical space sensor layout of the technological process flow is mapped into the spatial graph structure, where each sensor is regarded as a node and these nodes are connected by the k nearest neighbor algorithm. Subsequently, according to the mechanism knowledge, the sensors in the process are divided into different importance categories and weight coefficients are assigned to their nodes. The similarities between these weighted nodes are calculated, and the resulting edge information are used to construct the SWGs. Finally, the SWGs are input to a graph convolutional network, facilitating fault representation learning for fault diagnosis of complex process industry. Validation experiments are conducted using public industrial datasets, and the results demonstrate that the proposed method can effectively integrate the process knowledge to improve the fault diagnosis accuracy of the model.

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Graph neural network-based fault diagnosis: a review

Cited by 11 publications

References 52 publications

Graph neural networks for image‐guided disease diagnosis: A review

Graph neural networks for image‐guided disease diagnosis: A review

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Spatial weighted graph-driven fault diagnosis of complex process industry considering technological process flow

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