Power device reliability is a major concern during operation under extreme environments, as doing so reduces the operational lifetime of any power system or sensing infrastructure. Due to a potential for system failure, devices must be experimentally validated before implementation, which is expensive and time-consuming. In this paper, we have utilized machine learning algorithms to predict device reliability, significantly reducing the need for conducting experiments. To train the models, we have tested 224 power devices from 10 different manufacturers. First, we describe a method to process the data for modeling purposes. Based on the in-house testing data, we implemented various ML models and observed that computational models such as Gradient Boosting and LSTM encoder-decoder networks can predict power device failure with high accuracy.

中文翻译：

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使用机器学习算法预测极端条件下电力电子设备的可靠性

在极端环境下运行期间，功率设备可靠性是一个主要问题，因为这样做会缩短任何电力系统或传感基础设施的运行寿命。由于存在系统故障的可能性，必须在实施前对设备进行实验验证，这既昂贵又耗时。在本文中，我们利用机器学习算法来预测设备可靠性，显着减少了进行实验的需要。为了训练模型，我们测试了来自 10 个不同制造商的 224 个功率设备。首先，我们描述了一种为建模目的处理数据的方法。基于内部测试数据，我们实现了各种 ML 模型，并观察到梯度提升和 LSTM 编码器-解码器网络等计算模型可以高精度预测功率设备故障。