基于概率稀疏自注意力的IGBT模块剩余寿命跨工况预测  

Remaining Useful Life Prediction of IGBT Modules Across Working Conditions Based on ProbSparse Self-Attention

作　　者：钟智伟 王誉翔 黄亦翔[1] 肖登宇 夏鹏程 刘成良[1] ZHONG Zhiwei;WANG Yuxiang;HUANG Yixiang;XIAO Dengyu;XIA Pengcheng;LIU Chengliang(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240

出　　处：《上海交通大学学报》2023年第8期1005-1015,共11页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金(51975356)资助项目。

摘　　要：为提高绝缘栅双极型晶体管(IGBT)模块跨工况剩余寿命的预测精度以提升其可靠性,针对不同工况下IGBT模块的瞬态热阻特征提出一种基于概率稀疏自注意力机制和迁移学习的剩余使用寿命预测方法.搭建了IGBT模块加速老化试验台,在不同温度区间进行IGBT模块功率循环实验,采集不同工况下该模块全生命周期状态数据,计算获得IGBT模块衰退过程中的瞬态热阻变化数据,提取并筛选能准确反映IGBT模块老化状态的瞬态热阻特征,并使用所提方法开展跨工况剩余使用寿命预测.实验结果表明,提出的IGBT模块剩余寿命的跨工况预测方法精度明显优于其他对比方法,特别是IGBT模块早期衰退过程中的剩余寿命预测精度得到了显著提升.In order to improve the accuracy of remaining useful life(RUL)prediction of insulated gate bipolar transistor(IGBT)modules across working conditions to enhance their reliability,an RUL prediction method based on the ProbSparse self-attention mechanism and transfer learning was proposed based on the transient thermal resistance features of IGBT modules under different working conditions.An accelerated aging test bench of the IGBT module was designed ang built to perform power cycling experiments in different temperature ranges,and state data of full life-time under different working conditions were collected.Transient thermal resistance change data during the IGBT module degradation were calculated,and the transient thermal features that can accurately reflect the aging state of the IGBT module were extracted and selected.These features were used to predict the RUL of IGBT modules across different working conditions based on the proposed method.The experimental result shows that the accuracy of the proposed RUL prediction method of IGBT modules across working conditions outperforms other compared methods.Particularly,the RUL prediction accuracy during the early degradation stage has been significantly improved.

关 键 词：绝缘栅双极型晶体管模块 瞬态热阻 剩余寿命预测 概率稀疏自注意力 迁移学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN322.8[自动化与计算机技术—控制科学与工程]