Ensemble learning for predicting degradation under time-varying environment

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.