Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge

Chen, Zhiwen; Jia-dong, XU; Peng, Tao; Yang, Chunhua

doi:10.1109/tcyb.2021.3059002

Cited by 160 publications

(55 citation statements)

References 27 publications

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“…Three variants of GCNs, clustering, selection GCN, and aggregation GCN that are considered in this paper, demonstrate the superior performance than conventional neural networks. Moreover, the work [104] proposes a GCN-based method for context anomaly detection (fault detection). In particular, a structural analysis was first used to convert pre-diagnose results into graphs.…”

Section: B Contextualmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: B Contextualmentioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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“…There are three steps in the complete motor fault diagnosis process: fault detection, classification, and severity prediction. Among all the motor fault diagnosis methods, deep learning models are usually unable to build an end-to-end model due to the difficulty involved in obtaining training data and their poor anti-noise ability, so artificial features are required [16][17][18]. However, deep learning models have a strong representation ability and can be used as a part of a diagnosis method for feature preprocessing and other operations.…”

Section: Preliminariesmentioning

confidence: 99%

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

et al. 2022

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In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the driving devices. Consequently, maintaining the symmetry of the system relies on the normal and stable operation of the PMSM, and it is necessary to diagnose faults in the PMSM in a timely manner. In PMSM fault diagnosis methods, frequency domain features of the stator current are extensively used. However, these features change with the switching of motor operating conditions, leading to difficulty of diagnosis in multiple operating conditions. Therefore, a fault diagnosis method based on a convolutional neural network (CNN) phase tracker is proposed in this paper. Through phase tracking and angular domain resampling, the fundamental frequency of stator currents in different operating conditions are aligned, so as to fix the distribution of frequency domain features and solve the problem of features changing with operating conditions. Experimental results show that the proposed method can resample the stator current signals with a small error, detect faults in a relatively short time with high accuracy, and diagnose fault type and severity level under multiple operating conditions.

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“…In [64], the idea of using prior knowledge to construct the association graph is considered. It uses structural analysis (SA) method to prediagnose the fault, and transforms the results of prediagnosis into a graph to construct the GCN model for the final fault diagnosis.…”

Section: B Using Prior Knowledge To Construct the Association Graphmentioning

confidence: 99%

Graph neural network-based fault diagnosis: a review

Chen¹,

Xu²,

Alippi³

et al. 2021

Preprint

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Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs. Indeed, this particular representation form has led to superior performance compared to traditional FD approaches. In this review, an easy introduction to GNN, potential applications to the field of fault diagnosis, and future perspectives are given. First, the paper reviews neural network-based FD methods by focusing on their data representations, namely, time-series, images, and graphs. Second, basic principles and principal architectures of GNN are introduced, with attention to graph convolutional networks, graph attention networks, graph sample and aggregate, graph autoencoder, and spatial-temporal graph convolutional networks. Third, the most relevant fault diagnosis methods based on GNN are validated through the detailed experiments, and conclusions are made that the GNN-based methods can achieve good fault diagnosis performance. Finally, discussions and future challenges are provided.

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Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge

Cited by 160 publications

References 27 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

Graph neural network-based fault diagnosis: a review

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