A High-Efficiency IGBT Health Status Assessment Method Based on Data Driven

Author(s):  
Zhongqing Zhang ◽  
Guicui Fu ◽  
Bo Wan ◽  
Maogong Jiang ◽  
Yanruoyue Li
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Chao Cheng ◽  
Ming Liu ◽  
Bangcheng Zhang ◽  
Xiaojing Yin ◽  
Caixin Fu ◽  
...  

It is very important for the normal operation of high-speed trains to assess the health status of the running gear system. In actual working conditions, many unknown interferences and random noises occur during the monitoring process, which cause difficulties in providing an accurate health status assessment of the running gear system. In this paper, a new data-driven model based on a slow feature analysis-support tensor machine (SFA-STM) is proposed to solve the problem of unknown interference and random noise by removing the slow feature with the fastest instantaneous change. First, the relationship between various statuses of the running gear system is analyzed carefully. To remove the random noise and unknown interferences in the running gear systems under complex working conditions and to extract more accurate data features, the SFA method is used to extract the slowest feature to reflect the general trend of system changes in data monitoring of running gear systems of high-speed trains. Second, slowness data were constructed in a tensor form to achieve an accurate health status assessment using the STM. Finally, actual monitoring data from a running gear system from a high-speed train was used as an example to verify the effectiveness and accuracy of the model, and it was compared with traditional models. The maximum sum of squared resist (SSR) value was reduced by 16 points, indicating that the SFA-STM method has the higher assessment accuracy.


Author(s):  
I.G. Pogorelova ◽  
G. Amgalan

In this article presentsthe key findings of health status assessments of urban and rural school children aged 7–16 years based on the materials of comprehensive medical examination and statistical reporting in dynamics 2010–2014. Based on the study results were determined the health status groups and leading causes of morbidity among surveyed school children studying in urban and rural educational institutions of Mongolia. Study results showed that the number of children classified in third group of health was increased with the age of students and incidence of diseases of respiratory, digestive, neurological systems, and diseases of ear nose thought and vision organs were more common among the urban and rural school children of Mongolia.


Author(s):  
Jun Zhan ◽  
Ronglin Wang ◽  
Lingzhi Yi ◽  
Yaguo Wang ◽  
Zhengjuan Xie

The output power of wind turbine has great relation with its health state, and the health status assessment for wind turbines influences operational maintenance and economic benefit of wind farm. Aiming at the current problem that the health status for the whole machine in wind farm is hard to get accurately, in this paper, we propose a health status assessment method in order to assess and predict the health status of the whole wind turbine, which is based on the power prediction and Mahalanobis distance (MD). Firstly, on the basis of Bates theory, the scientific analysis for historical data from SCADA system in wind farm explains the relation between wind power and running states of wind turbines. Secondly, the active power prediction model is utilized to obtain the power forecasting value under the health status of wind turbines. And the difference between the forecasting value and actual value constructs the standard residual set which is seen as the benchmark of health status assessment for wind turbines. In the process of assessment, the test set residual is gained by network model. The MD is calculated by the test residual set and normal residual set and then normalized as the health status assessment value of wind turbines. This method innovatively constructs evaluation index which can reflect the electricity generating performance of wind turbines rapidly and precisely. So it effectively avoids the defect that the existing methods are generally and easily influenced by subjective consciousness. Finally, SCADA system data in one wind farm of Fujian province has been used to verify this method. The results indicate that this new method can make effective assessment for the health status variation trend of wind turbines and provide new means for fault warning of wind turbines.


1997 ◽  
Vol 18 (3) ◽  
pp. 1-3
Author(s):  
Geok Lin Khor ◽  
Panata Migasena ◽  
Keyou Ge ◽  
Rainer Gross ◽  
Adriane Lacle ◽  
...  

After the experiences at the individual sites during the Reconnaissance project had been presented in a plenary format, five groups were formed. Four of these groups examined the individual questions on the original questionnaire with specific attention to (1) nutritional status and biological variables, (2) food intake and food security variables, (3) health status assessment variables, and (4) lifestyle, socio-demographic, and social behaviour and practices variables. In this process, the groups identified items on the questionnaire that had been found, at one or another site, to be of doubtful appropriateness or feasibility. The area of concern of the fifth group was the sampling frame and selection procedures for sites and individuals within sites.


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