A Semi-incremental Recognition Method for On-Line Handwritten English Text

2014 ◽  
Author(s):  
Cuong Tuan Nguyen ◽  
Bilan Zhu ◽  
Masaki Nakagawa
Keyword(s):  
English Text ◽  
Recognition Method ◽  
On Line ◽  
Incremental Recognition

scholarly journals Electrostatic Sensor Application for On-Line Monitoring of Wind Turbine Gearboxes

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3574 ◽  
Author(s):  
Huijie Mao ◽  
Hongfu Zuo ◽  
Han Wang
Keyword(s):  
Wind Turbine ◽  
Wear Debris ◽  
Bench Test ◽  
Recognition Method ◽  
Wind Turbine Gearbox ◽  
Practical Applications ◽  
Electrostatic Induction ◽  
Oil Flow ◽  
On Line ◽  
Application Potential

The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications.

Pattern Recognition for UHF Partial Discharge of Power Transformer Using LS-SVM

2012 ◽  
Vol 588-589 ◽  
pp. 384-387
Author(s):  
Jin Sha Yuan ◽  
Hai Kun Shang
Keyword(s):  
Pattern Recognition ◽  
High Frequency ◽  
Power Transformer ◽  
Partial Discharge ◽  
Small Samples ◽  
Support Vector ◽  
Discharge Diagnosis ◽  
Recognition Method ◽  
On Line ◽  
Pattern Recognition Approach

Partial discharge diagnosis is an important tool for detecting insulation defects in power equipments. This paper presents a pattern recognition approach based on Least Squares Support Vector Machine (LS-SVM) for Ultra High Frequency (UHF) partial discharge diagnosis of power transformer. Six different feature parameters were extracted from the data obtained from Partial Discharge (PD) on-line monitoring system. LS-SVM was used to discriminate between 4 different PD sources. Experimental results demonstrate that the proposed approach has higher recognition accuracy compared with traditional BPNN recognition method under condition of small samples, and has great potential for use of field data.

A New Intelligent Fault Recognition Method for Gearbox

2013 ◽  
Vol 684 ◽  
pp. 369-372
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Yan Jie Zhou ◽  
Ya Song Pu
Keyword(s):  
Wavelet Decomposition ◽  
Sample Entropy ◽  
Recognition Method ◽  
Fault Pattern ◽  
Multi Scale ◽  
Fault Recognition ◽  
Gear Fault ◽  
On Line ◽  
Novel Method ◽  
Undecimated Wavelet

In this paper, a novel method to recognize gear fault pattern was approached based on multi-scale morphological undecimated wavelet decomposition, sample entropy and grey incidence. Firstly, multi-scale morphological undecimated wavelet decomposition was developed based on the characteristic of impulse feature extraction in difference morphological filter. And it was used to process different gear fault signals in five levels. Secondly, the sample entropy of each level was calculated. Finally, the sample entropy was served as the feature vectors and the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical example shows the efficiency of the proposed recognition method. It is suitable for on-line monitoring and fault diagnosis of gear.

The Online Pattern Recognition Method of SF6 Gas Leakage Based on Image Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 682-685
Author(s):  
Jin Xin Huang ◽  
Ya Jin Li ◽  
Yang Jiang ◽  
Jie Zhan ◽  
Lin Niu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Automatic Identification ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Gas Leakage ◽  
Feature Extracting ◽  
Line Identification ◽  
Testing Data ◽  
On Line ◽  
Method Paper

Based on image processing of SF6 gas leakage on-line pattern recognition method, this paper achieves gas leakage feature extracting, on-line identification of gas leakage and leakage points, SF6 gas leakage can be on-line automatic identification. The simulation results show the feasibility of the algorithm. Compared with the traditional method, paper provides a more intuitive discrimination basis for field staff , as well as for the depth of the late testing data mining provides a research way of thinking

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