Research on Vibration Detection Technology for Mechanical Defects of GIS Equipment

2021 ◽  
Keyword(s):  
Vibration Detection ◽  
Detection Technology

scholarly journals Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 56
Author(s):  
Yongye Wu ◽  
Zhanlong Zhang ◽  
Rui Xiao ◽  
Peiyu Jiang ◽  
Zijian Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optimization Algorithm ◽  
Network Optimization ◽  
Extraction Method ◽  
Deep Belief Network ◽  
Vibration Signals ◽  
Belief Network ◽  
Feature Extraction Method ◽  
Vibration Detection ◽  
Detection Technology

The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals’ eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals’ eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.

On-line image readout in CTEM

1994 ◽  
Vol 52 ◽  
pp. 400-401
Author(s):  
K.-H. Herrmann ◽  
W. D. Rau ◽  
R. Sikeler
Keyword(s):  
Primary Electron ◽  
Digital Information ◽  
Electron Hole ◽  
Detection Technology ◽  
Long Time ◽  
On Line ◽  
Electron Image ◽  
Optical Transfer ◽  
Detection Quantum Efficiency ◽  
Technical Solutions

Quantitative recording of electron patterns and their rapid conversion into digital information is an outstanding goal which the photoplate fails to solve satisfactorily. For a long time, LLL-TV cameras have been used for EM adjustment but due to their inferior pixel number they were never a real alternative to the photoplate. This situation has changed with the availability of scientific grade slow-scan charged coupled devices (CCD) with pixel numbers exceeding 106, photometric accuracy and, by Peltier cooling, both excellent storage and noise figures previously inaccessible in image detection technology. Again the electron image is converted into a photon image fed to the CCD by some light optical transfer link. Subsequently, some technical solutions are discussed using the detection quantum efficiency (DQE), resolution, pixel number and exposure range as figures of merit.A key quantity is the number of electron-hole pairs released in the CCD sensor by a single primary electron (PE) which can be estimated from the energy deposit ΔE in the scintillator,

New Defect Detection Technology for Tissue Industry

2014 ◽  
Vol 68 (3) ◽  
pp. 311-313
Author(s):  
Jason Zyglis ◽  
Wayne Killmer ◽  
Atsushi Kurosaki
Keyword(s):  
Defect Detection ◽  
Detection Technology
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