scholarly journals Real-Time Fault Detection to Ensure the Safe Operation of the Single-Phase Five-Level VIENNA Rectifier

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8308
Author(s):  
Pham Thi Thuy Linh ◽  
Nguyen Ngoc Bach ◽  
Vu Minh Phap ◽  
Doan Van Binh
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Single Phase ◽  
Control Method ◽  
Low Voltage ◽  
Level Structure ◽  
Point Of View ◽  
Vienna Rectifier ◽  
Diagnosis Method ◽  
Lab Test

This work aims to explore and evaluate the nonreversible AC/DC five-level structure from the point of view of its operational safety: high electrical security on internal destruction and continuity in operation. It only has low-voltage monotransistor cells (Si and SiC 600 V max) and is intrinsically tolerant to imperfection control and parasites, therefore naturally secure. The design and lab-test of fault monitoring and fault diagnosis with just one voltage sensor of a single-phase five-level VIENNA rectifier were proposed. This real-time diagnostic method allows for a safe stop or corrective control strategy based on the reconfiguration of the modulation. The reconstruction strategy allows for optimization of the current and voltage signals as well as power factor. A continuous post-fault operation can be achieved for critical applications. An experimental prototype 3 kW/230 VAC/800 VDC/32 kHz was created to validate the proposed fault diagnosis method and reconfiguration control method.

scholarly journals A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

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.

scholarly journals Real-time Fault Diagnosis Method of Battery System Based on Shannon Entropy

2017 ◽  
Vol 105 ◽  
pp. 2354-2359 ◽  
Author(s):  
Zhenyu Sun ◽  
Peng Liu ◽  
Zhenpo Wang
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Shannon Entropy ◽  
Battery System ◽  
Diagnosis Method
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