关键词: 剩余使用寿命预测, 整群抽样, 支持向量回归, 半导体激光器

Abstract: RUL prediction was the core problem of reliability evaluation of high-power semiconductor lasers under various environmental stresses. In practical applications， the existing SVR methods all focused on minimizing the overall errors of the regression curve of the trained model， so as to pursue the generalization， which often resulted in unsatisfactory prediction results at the critical early warning stage， especially before the near-failure， and failed to ensure its reliability. Therefore， the SVR model training method was proposed based on cluster sampling. The observation data in the later period of the test samples were sampled for multiple cluster and then used for the SVR model test. The parameters in the SVR model made the SVR model fit the data in the later period of the training samples better. The effectiveness and robustness of the proposed method were verified by a case study， the results show that the performance and practical value of the proposed method are better than those of several representative small sample analysis methods.

Key words: remaining useful life（RUL）prediction, cluster sampling, support vector regression（SVR）, semiconductor laser