Improved Bayes-Based Reliability Prediction of Small-Sample Hall Current Sensors

Abstract

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.