Research on Accelerated Degradation Test Design and Life Prediction for Aluminum Electrolytic Capacitor

As a life sensitive device, aluminum electrolytic capacitor degrades its parameters to a certain extent with the passage of time, which will inevitably affect the life of electronic board, so it is very important to study its degradation law and life characteristics. Starting from the structure and degradation mechanism of aluminum electrolytic capacitor, this paper designs an accelerated degradation test with temperature as the accelerated sensitive stress, uses the accelerated degradation data for life prediction, and gives a top-down design direction for extending the life of aluminum electrolytic capacitors. From the perspective of the demand for repair according to condition, a method for predicting the remaining life of aluminum electrolytic capacitors based on BP neural network is proposed. The source of prediction data can be field measured data or accelerated test data. By comparing the degradated capacitance predicted by BP neural network with the measured data of the degradation test and the predicted value of the least squares linear fitting of the experimental data, the results show that the prediction error of capacitance based on the BP neural network is within 3%, while the predicted value of the least squares linear fitting is around 6%, which verifies the superiority of the BP neural network life prediction algorithm, and provides strong support for development and application of the subsequent on-line monitoring technology of board level capacitors.