Ting Chen
Research Subjects
Research Interests
Ting Chen has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ling Ju
Taizhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Taizhou 225306, China
1 shared publication - Shike Wei
School of Electrical & Automation Engineering NNU, Nanjing Normal University, Nanjing 210000, China
1 shared publication - Yongling Lu
Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd, NanJing, China, China
1 shared publication - Zhengyu Liu
Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
1 shared publication
Latest Publications
- Open Access
Improved Bayes-Based Reliability Prediction of Small-Sample Hall Current Sensors
As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model.
Open AccessImproved Bayes-Based Reliability Prediction of Small-Sample Hall Current Sensors
As a type of magnetic sensor known for its high reliability and long lifespan, the reliability issues of Hall current sensors have attracted attention in fields such as electromagnetic compatibility. However, there is still a lack of sufficient failure data for reliability prediction. Therefore, a small-sample reliability prediction method based on the improved Bayes method is proposed. Firstly, the pseudo-failure lifespan data are acquired through the accelerated degradation testing of Hall current sensors subjected to temperature and humidity stressors, and the life is examined by the Weibull distribution; then, the data expanded using the BP neural network model are used as the a priori information, and the parameter estimation of the Weibull distribution is obtained by the Bootstrap method and Gibbs sampling; finally, the Peck accelerated model is implemented to achieve the normal temperature-humidity reliability prediction of Hall current sensors under stress, and the utility of the enhanced Bayes technique is confirmed through the application of the Wiener stochastic process model.