Power electronic devices are very important component of power processing circuits. However, sometimes under circuit overstress, they may face abrupt failures. Lifetime prediction is needed to prevent these sudden failures in power devices. However, the random noise and errors in the measurement data make the existing methods have large prediction errors. This paper proposes a fusion method based on Least Squares Support Vector Machines (LSSVM)-Particle Filter (PF) that can accurately and stably predict the Remaining Useful Life (RUL) of Insulated gate bipolar transistor (IGBT). First, the method uses the LSSVM model to extract the degraded non-linear feature. Then, the linear regression model is used to extract the degraded linear features. Finally, the PF algorithm is used to fuse the two features to obtain more accurate prediction results and uncertainty expression. The method of feature extraction and fusion is used to effectively eliminate the interference of random noise and errors, so it has more accurate and stable prediction results. The online aging data of the IGBT is used to verify the algorithm, and the results prove that the algorithm can more accurately and stably predict the status or life of IGBT. This method provides a new perspective to solve the problem of life prediction.

电力电子设备是电力处理电路的重要组成部分。但是，有时在电路超负荷下，它们可能会遇到突然的故障。需要进行寿命预测以防止功率设备中的这些突然故障。然而，测量数据中的随机噪声和误差使得现有方法具有较大的预测误差。本文提出了一种基于最小二乘支持向量机（LSSVM）-粒子滤波器（PF）的融合方法，该方法可以准确，稳定地预测绝缘栅双极型晶体管（IGBT）的剩余使用寿命（RUL）。首先，该方法使用LSSVM模型提取降级的非线性特征。然后，使用线性回归模型提取退化的线性特征。最后，PF算法用于融合这两个特征，以获得更准确的预测结果和不确定性表示。特征提取与融合方法有效地消除了随机噪声和误差的干扰，具有更加准确，稳定的预测结果。通过IGBT在线老化数据对算法进行验证，结果证明该算法可以更准确，更稳定地预测IGBT的状态或寿命。该方法为解决寿命预测问题提供了新的视角。实验结果表明，该算法可以更准确，稳定地预测IGBT的状态或寿命。该方法为解决寿命预测问题提供了新的视角。实验结果表明，该算法可以更准确，稳定地预测IGBT的状态或寿命。该方法为解决寿命预测问题提供了新的视角。