scholarly journals A compressed sensing and CNN‐based method for fault diagnosis of photovoltaic inverters in edge computing scenarios

2022 ◽  
Author(s):  
Xinyi Wang ◽  
Bo Yang ◽  
Zhaojian Wang ◽  
Qi Liu ◽  
Cailian Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Compressed Sensing ◽  
Edge Computing

Fault diagnosis of motor based on low cost edge computing technology

2020 ◽  
Author(s):  
Yi-Horng Lai ◽  
Ye-Cheng Zhang ◽  
Liang Fang ◽  
Chiao-Sheng Wang ◽  
Jau-Woei Perng
Keyword(s):  
Fault Diagnosis ◽  
Low Cost ◽  
Edge Computing ◽  
Computing Technology

scholarly journals A Multifault Diagnosis Method of Gear Box Running on Edge Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiao-ping Zhao ◽  
Yong-hong Zhang ◽  
Fan Shao
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Data Center ◽  
Edge Computing ◽  
Industrial Equipment ◽  
Point Model ◽  
Computing Device ◽  
External Data ◽  
Diagnosis Method ◽  
Analyze Data

In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

Edge Computing: A Promising Framework for Real-Time Fault Diagnosis and Dynamic Control of Rotating Machines Using Multi-Sensor Data

2019 ◽  
Vol 19 (11) ◽  
pp. 4211-4220 ◽  
Author(s):  
Gang Qian ◽  
Siliang Lu ◽  
Donghui Pan ◽  
Huasong Tang ◽  
Yongbin Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Dynamic Control ◽  
Edge Computing ◽  
Sensor Data ◽  
Rotating Machines
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