scholarly journals Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

2021 ◽  
pp. 1-1
Author(s):  
Xiaoxian Wang ◽  
Siliang Lu ◽  
Wenbin Huang ◽  
Qunjing Wang ◽  
Shiwu Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Data Reduction ◽  
Industrial Internet Of Things ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Industrial Internet ◽  
Efficient Data

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

2021 ◽  
pp. 1-1
Author(s):  
Youcef Djenouri ◽  
Asma Belhadi ◽  
Gautam Srivastava ◽  
Uttam Ghosh ◽  
Pushpita Chatterjee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Learning Framework ◽  
Industrial Internet

scholarly journals An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hanrui Zhang ◽  
Qianmu Li ◽  
Shunmei Meng ◽  
Zhuoran Xu ◽  
Chaoxian Lv
Keyword(s):  
Dynamic Programming ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Ensemble Learning ◽  
Industrial Internet Of Things ◽  
Industrial Control ◽  
Adjustment Method ◽  
Control Equipment ◽  
Industrial Internet ◽  
Diagnosis Model

With the rapid development of intelligent manufacturing and Industrial Internet of Things, many industrial control systems have high requirements for the security of the system itself. Failures of industrial control equipment will cause abnormal operation of industrial control equipment and waste of resources. It is very meaningful to detect and identify potential equipment abnormalities and failures in time and implement effective fault tolerance strategies. In the Industrial Internet of Things environment, the instructions and parameters of industrial control equipment often change due to changes in actual requirements. However, it is impractical to customize the learning method for each parameter value. Aiming at the problem, this paper proposes a fault diagnosis model based on ensemble learning and proposes a method of updating voting weights based on dynamic programming to assist decision-making. This method is based on Bagging strategy and combined with dynamic programming voting weight adjustment method to complete fault type prediction. Finally, this paper uses different loads as dynamic conditions; the diagnostic capability of the Bagging-based fault diagnosis integrated model in a dynamically changing industrial control system environment is verified by experiments. The fault diagnosis model of industrial control equipment based on ensemble learning effectively improves the adaptive ability of the model and makes the fault diagnosis framework truly intelligent. The voting weight adjustment method based on dynamic programming further improves the reliability of voting.

Achieving Accountable and Efficient Data Sharing in Industrial Internet of Things

2021 ◽  
Vol 17 (2) ◽  
pp. 1416-1427
Author(s):  
Cheng Huang ◽  
Dongxiao Liu ◽  
Jianbing Ni ◽  
Rongxing Lu ◽  
Xuemin Shen
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Efficient Data
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