In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium‐ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances.

中文翻译：

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使用高斯过程回归和诱导有序加权平均算子估算剩余使用寿命的新融合和选择方法

在本文中，我们提出了新的融合和选择方法来准确预测剩余使用寿命。融合方案是基于一组高斯过程回归模型传递的结果的组合而构建的。每个回归变量都有自己的协方差函数和初始超参数。在这种情况下，我们采用诱导有序加权平均作为融合工具来实现这种组合。除了选择方法以外，还实现了基于简单平均和有序加权平均算子的两种附加融合技术。原始数据的相邻元素之间的差异用于训练，而不是原始值。对锂离子电池数据进行的实验结果表明，获得的结果有了显着改善。