Fa-Mb-ResNet for Grounding Fault Identification and Line Selection in the Distribution Networks

Yang, Liulin; Yu, Li; Wei, Zhi

doi:10.1109/jiot.2021.3131171

Cited by 17 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rise of Internet of things (IoT), many of IoT techniques are used in different fields (eg., 5G [13], unmanned aerial vehicle [14], and fault diagnosis [15][16][17][18][19]). Meanwhile, edge computing is a new computing paradigm that enables fast detection through the deployment of algorithms that are embedded in distributed nodes [20].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Zhu

Wang

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99 %. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Zhu

Wang

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Fault diagnosis has seen extensive research on deep learning methods [18] [19] [20] [21], including deep belief networks (DBNs) [22] [23] [24], stacked autoencoders (SAEs) [25], convolutional neural networks (CNNs) [26] [27] [28] [29], and recurrent neural networks. Deep neural networks compensate for the limitations of shallow learning machines in feature extraction and can adaptively identify hidden features, reducing the reliance of fault diagnosis algorithms on data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

RMCW: An Improved Residual Network With Multi-Channel Weighting for Machinery Fault Diagnosis

Liu,

Yu,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Faced with increasingly complex industrial data, standard machine learning algorithms struggle to effectively extract both linear and nonlinear features. In this study, an improved residual network (ResNet) called Residual network with Independent Multi-Channel Weighting (RMCW) to tackle the nonlinear, temporally uncertain, and unevenly distributed fault. Firstly, a strategy for constructing the multi-channel vibration intrinsic mode function (IMF) images is designed to obtain the primary features by combing the empirical mode decomposition (EMD) and the gramian angular field (GAF). Secondly, a dynamic receptive field (DRF) with independent channel weighting is utilized to adaptively fuse the multi-channel features. This renders both initialization parameters for each individual channel and DRF parameters mutually independently adaptive to the fault features in the different batch. Thirdly, the RMCW model is built by inputting the fused features to the network of 9 residual building blocks. Two experimental cases verify that the propose method is effective for the machinery fault diagnosis and is superior to the comparing methods. INDEX TERMSFeature fusion; fault diagnosis; deep learning; vibration signal.

show abstract

Fault type identification of arc grounding based on time-frequency domain characteristics of zero sequence current

Liu

Yang

Tang³

et al. 2023

Electric Power Systems Research

View full text Add to dashboard Cite

Fa-Mb-ResNet for Grounding Fault Identification and Line Selection in the Distribution Networks

Cited by 17 publications

References 27 publications

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

RMCW: An Improved Residual Network With Multi-Channel Weighting for Machinery Fault Diagnosis

Fault type identification of arc grounding based on time-frequency domain characteristics of zero sequence current

Contact Info

Product

Resources

About