Product lifetime prediction is challenging when the product is subject to a time‐varying operational environment. Most of the existing studies use some functions to explicitly specify the relationship between degradation parameters and environmental conditions so as to reveal how the degradation process evolves over time. However, in many applications, the assumptions needed for establishing these functions cannot be validated in engineering practice or they cannot accurately model the entire underlying degradation mechanism. In contrast to previous work, the focus of our study is placed on product degradation prognosis by implementing an ensemble learning method. This method combines the stochastic process modeling approach and the machine learning approach, taking advantage of these approaches to gain a more accurate and stable degradation prediction. The proposed method is demonstrated by some simulation examples and by a case study of lithium‐ion battery accelerated degradation test. Both the simulation study and the real case verify the superiority of the proposed method. The case study indicates that the ensemble learning method can further help to effectively manage the energy storage and energy distribution of battery packs.

中文翻译：

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整合学习以预测时变环境下的退化

当产品处于时变操作环境中时，产品寿命预测将具有挑战性。现有的大多数研究都使用一些功能来明确指定降解参数与环境条件之间的关系，以揭示降解过程如何随时间演变。但是，在许多应用中，建立这些功能所需的假设无法在工程实践中得到验证，或者无法准确地对整个潜在的降级机制进行建模。与以前的工作相比，我们的研究重点是通过实施整体学习方法来关注产品退化的预后。这种方法结合了随机过程建模方法和机器学习方法，利用这些方法获得更准确和稳定的降级预测。通过一些仿真实例和锂离子电池加速降解测试的案例研究证明了该方法的有效性。仿真研究和实际案例均证明了该方法的优越性。案例研究表明，集成学习方法可以进一步帮助有效地管理电池组的能量存储和能量分配。