Capacitors are widely used in electronic systems and have a key function in electromagnetic compatibility (EMC) compliance. However, the aging of capacitors results in an alteration of their parameters, which could pose a threat on the normal operation of systems as well as their EMC compliance. Normally, accelerated aging is employed to shorten the experiment time. After the aging, the capacitance and equivalent series resistance (ESR) are measured to evaluate the aging process. In this article, a new continuous characterization measurement setup is implemented in which the accelerated aging of the capacitors under test (CUTs) is continuously monitored during the overall accelerated aging process. It significantly improves the continuity of the measurement and eliminates the errors attributed to the interrupting of the aging process. This method is validated by comparing measurement results from the new measurement method with the results of the conventional method. This was done by subjecting two types of film capacitors to thermal and electrical stress in order to evaluate the accelerated aging effects. Furthermore, a conditional deep neural network with a dropout technique is proposed to predict the accelerated aging conditions of the capacitors. Instead of only forecasting the failure threshold, the proposed network is able to dynamically predict the accelerated aging conditions at different elevated temperatures and voltages. This leads to a serious reduction in the total measurement time from 1000 to 200 h.

中文翻译：

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基于高级测量和深度神经网络技术的电容器加速老化预测

电容器广泛用于电子系统，并在电磁兼容性 (EMC) 合规性方面发挥关键作用。然而，电容器的老化会导致其参数发生变化，这可能对系统的正常运行及其 EMC 合规性构成威胁。通常，采用加速老化来缩短实验时间。老化后，通过测量电容和等效串联电阻 (ESR) 来评估老化过程。在本文中，实施了一种新的连续表征测量设置，其中在整个加速老化过程中连续监测被测电容器 (CUT) 的加速老化。它显着提高了测量的连续性，并消除了因老化过程中断而导致的错误。通过将新测量方法的测量结果与传统方法的测量结果进行比较来验证该方法。这是通过使两种类型的薄膜电容器承受热应力和电应力来评估加速老化效果来完成的。此外，还提出了一种具有 dropout 技术的条件深度神经网络来预测电容器的加速老化条件。所提出的网络不仅能够预测故障阈值，还能够动态预测不同升高温度和电压下的加速老化条件。这导致总测量时间从 1000 小时大幅减少到 200 小时。这是通过使两种类型的薄膜电容器承受热应力和电应力来评估加速老化效果来完成的。此外，提出了一种具有辍学技术的条件深度神经网络来预测电容器的加速老化条件。所提出的网络不仅能够预测故障阈值，还能够动态预测不同升高温度和电压下的加速老化条件。这导致总测量时间从 1000 小时大幅减少到 200 小时。这是通过使两种类型的薄膜电容器承受热应力和电应力来评估加速老化效果来完成的。此外，提出了一种具有辍学技术的条件深度神经网络来预测电容器的加速老化条件。所提出的网络不仅能够预测故障阈值，还能够动态预测不同升高温度和电压下的加速老化条件。这导致总测量时间从 1000 小时大幅减少到 200 小时。提议的网络能够动态预测不同升高温度和电压下的加速老化条件。这导致总测量时间从 1000 小时大幅减少到 200 小时。提议的网络能够动态预测不同升高温度和电压下的加速老化条件。这导致总测量时间从 1000 小时大幅减少到 200 小时。