scholarly journals Fault Diagnosis of Capacitance Aging in DC Link Capacitors of Voltage Source Inverters Using Evidence Reasoning Rule

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Linhao Liao ◽  
Haibo Gao ◽  
Yelan He ◽  
Xiaobin Xu ◽  
Zhiguo Lin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Voltage Source ◽  
Neural Network Approach ◽  
Dc Voltage ◽  
Voltage Source Inverters ◽  
Failure State ◽  
Diagnosis Method ◽  
Relative Weighting ◽  
Different Levels

Capacitance aging of DC link capacitors in voltage source inverters (VSIs) is a common fault which can lead to instability of the DC voltage. In such a failure state, although the VSI can still work, its performance gradually deteriorates, resulting in a shorter service life of the equipment. Here, an online monitoring and fault diagnosis method for capacitance aging based on evidence reasoning (ER) rule is presented. Features from the DC link voltage data with different levels of capacitance aging are extracted, and data features are generated as pieces of diagnostic evidence, which are then combined according to the ER rule. Finally, capacitance aging fault levels were estimated using the combined results. This method has better diagnostic performance compared to a backpropagation (BP) neural network approach and can be used to flexibly define the relative weighting of each evidence parameter depending on the application. This approach can therefore be widely used for fault diagnosis of an array of different devices.

scholarly journals Open-Switch Fault Diagnosis Method in Voltage-Source Inverters Based on Phase Currents

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63619-63625 ◽  
Author(s):  
Zhang Jian-Jian ◽  
Chen Yong ◽  
Chen Zhang-Yong ◽  
Zhou Anjian
Keyword(s):  
Fault Diagnosis ◽  
Voltage Source ◽  
Voltage Source Inverters ◽  
Open Switch ◽  
Diagnosis Method

scholarly journals A Hybrid System Model-Based Open-Circuit Fault Diagnosis Method of Three-Phase Voltage-Source Inverters for PMSM Drive Systems

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1251 ◽  
Author(s):  
Tao Chen ◽  
Yuedou Pan ◽  
Zhanbo Xiong
Keyword(s):  
Fault Diagnosis ◽  
Hybrid System ◽  
Open Circuit ◽  
System Model ◽  
Voltage Source ◽  
Phase Voltage ◽  
Voltage Source Inverters ◽  
Three Phase ◽  
Diagnosis Method ◽  
Drive Systems

This paper presents an open-circuit (OC) fault diagnosis method of three-phase voltage-source inverters (VSIs) for permanent magnet synchronous motor (PMSM) drive systems based on the hybrid system model (HSM). On the basis of phase voltage analysis, the HSM of the PMSM-inverter system was established, which can describe the system more accurately. To quickly diagnose whether the fault occurs, a current estimator was constructed based on the healthy HSM. Different fault types of the VSI can be simulated synchronously by the HSM, and the similarity between each simulated fault type and the actual one is evaluated based on the Euclidean distance method. Therefore, the fault isolation can be carried out according to the characteristics of Euclidean distances. The proposed method has the advantages of fast diagnosis speed and strong robustness, and it does not require additional sensors. By the presented diagnosis algorithm, the fault can be detected and isolated within a time interval about 5% and 10% of the current fundamental wave period, respectively. The simulation and experimental results verify the effectiveness of the proposed method.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault diagnosis method for scintillation detector based on BP neural network

2021 ◽  
Vol 16 (07) ◽  
pp. T07006
Author(s):  
Y.X. Xie ◽  
Y.J. Yan ◽  
X. Li ◽  
T.S. Ding ◽  
C. Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Scintillation Detector ◽  
Diagnosis Method
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