整流器作为电能变换功率模块的关键环节，广泛应用于变频器等供电系统设备中，其运行可靠性直接影响整个系统的运行状态。为提高整流器运行可靠性，需针对其中的敏感部件整流二极管进行寿命预测，提出一种由改进后的灰狼优化算法（grey wolf optimization, GWO）优化简单循环单元（simple recurrent unit, SRU）网络超参数的预测模型。首先对二极管进行功率循环加速老化试验，对其特征参数进行分析，选取正向导通压降作为主要老化特征，其次采用改良策略后的GWO对SRU网络的学习率、隐藏层个数、迭代次数等超参数进行寻优，建立组合预测模型，最后将老化试验所得数据输入模型中进行训练以及验证，得到预测结果与其他模型对比。结果表明：本文模型具有更高的准确性，基于数据驱动的预测方法较传统分析建模方法提高了二极管寿命预测精度，进而提高了整流器工作可靠性。

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit(SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.