scholarly journals Rectifier Fault Diagnosis and Fault Tolerance of a Doubly Fed Brushless Starter Generator

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Liwei Shi ◽  
Zhou Bo
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Wavelet Packet ◽  
Energy Eigenvalue ◽  
Open Circuit ◽  
Fault Analysis ◽  
Identification Algorithm ◽  
Diagnosis Method ◽  
Doubly Fed ◽  
Starter Generator

This paper presents a rectifier fault diagnosis method with wavelet packet analysis to improve the fault tolerant four-phase doubly fed brushless starter generator (DFBLSG) system reliability. The system components and fault tolerant principle of the high reliable DFBLSG are given. And the common fault of the rectifier is analyzed. The process of wavelet packet transforms fault detection/identification algorithm is introduced in detail. The fault tolerant performance and output voltage experiments were done to gather the energy characteristics with a voltage sensor. The signal is analyzed with 5-layer wavelet packets, and the energy eigenvalue of each frequency band is obtained. Meanwhile, the energy-eigenvalue tolerance was introduced to improve the diagnostic accuracy. With the wavelet packet fault diagnosis, the fault tolerant four-phase DFBLSG can detect the usual open-circuit fault and operate in the fault tolerant mode if there is a fault. The results indicate that the fault analysis techniques in this paper are accurate and effective.

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

2013 ◽  
Vol 273 ◽  
pp. 300-304
Author(s):  
Xin Wang ◽  
Juan Xu ◽  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Open Circuit ◽  
Learning Performance ◽  
Cascaded Converter ◽  
Diagnosis Method ◽  
Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

scholarly journals Study by modeling and simulation of open-switch fault diagnosis for five-level converters

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Florent Becker ◽  
Ehsan Jamshidpour ◽  
Philippe Poure ◽  
Shahrokh Saadate
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Tolerant ◽  
Fault Localization ◽  
Open Circuit ◽  
System Reconfiguration ◽  
Open Switch ◽  
Diagnosis Method ◽  
Control Orders ◽  
Detection And Localization

In this paper, an open-switch fault diagnosis method for five-level H-Bridge Neutral Point Piloted (HB-NPP) or T-type converters is proposed. While fault tolerant operation is based on three steps (fault detection, fault localization and system reconfiguration), a fast fault diagnosis, including both fault detection and localization, is mandatory to make a suitable response to an open-circuit fault in one of the switches of the converter. Furthermore, fault diagnosis is necessary in embedded and safety critical applications, to prevent further damage and perform continuity of service.In this paper, we present an open-switch fault diagnosis method, based on the switches control orders and the observation of the converter output voltage level. In five-level converters such as HB-NPP and T-type topologies, some switches are mostly 'on' at the same time. Therefore, the fault localization is quite complicated. The fault diagnosis method we proposed is capable to detect and localize an open-switch fault in all cases. Computer simulations are carried out by using Matlab Simulink and SimPowerSystem toolbox to validate the proposed approach.

scholarly journals Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 753
Author(s):  
Ruirui Wang ◽  
Zhan Feng ◽  
Sisi Huang ◽  
Xia Fang ◽  
Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Short Term Memory ◽  
Wavelet Packet ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Reconstructed Signal ◽  
Voltage Signal ◽  
Diagnosis Method ◽  
Lstm Network

To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis.

Open‐circuit fault diagnosis for a fault‐tolerant three‐level neutral‐point‐clamped STATCOM

2019 ◽  
Vol 12 (4) ◽  
pp. 810-816 ◽  
Author(s):  
Haoyang Li ◽  
Yuanbo Guo ◽  
Jinhui Xia ◽  
Ze Li ◽  
Xiaohua Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Tolerant ◽  
Open Circuit ◽  
Neutral Point ◽  
Circuit Fault Diagnosis
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