Remaining useful life (RUL) prediction is crucial to supporting intelligent maintenance and health management of safety–critical products. Although advanced data-driven approaches such as deep neural networks are effective in processing high-dimensional non-linear health features, their application to field RUL prediction confronts with two challenges: (a) adaptivity of the lifetime parameter learning process is often restricted, and (b) prediction of multi-source uncertainties is almost analytically intractable. This paper addresses such challenges by devising a tractable, global adaptive model-data-interaction prognostic framework, where a non-linear stochastic degradation model governed by self-adaptive trajectory pattern is constructed to transfer historical health knowledge. In particular, a joint parameter learning framework is established under the structure of a multi-branch Bayesian network, such that to simultaneously learn: (a) degradation model parameters, and (b) network hyper-parameters. Additionally, the key control parameters of the degradation process are updated adaptively leveraging multi-dimensional sequential Bayesian learning. An efficient interpolation algorithm is further proposed to alleviate computation burden of RUL distributions. Case studies conducted on both turbofan engines degradation data and field train bearing vibration data demonstrate the superior model performance compared to existing methodologies.

中文翻译：

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混合预后框架：随机退化过程与自适应轨迹学习以转移历史健康知识


剩余使用寿命 （RUL） 预测对于支持安全关键型产品的智能维护和健康管理至关重要。尽管深度神经网络等先进的数据驱动方法在处理高维非线性健康特征方面很有效，但它们在现场 RUL 预测中的应用面临两个挑战：（a） 生命周期参数学习过程的适应性经常受到限制，以及 （b） 多源不确定性的预测在分析上几乎难以处理。本文通过设计一个可处理的、全局自适应模型-数据-交互预后框架来解决这些挑战，其中构建了一个由自适应轨迹模式控制的非线性随机退化模型来传递历史健康知识。特别是，在多分支贝叶斯网络的结构下建立了一个联合参数学习框架，以便同时学习：（a） 退化模型参数，以及 （b） 网络超参数。此外，利用多维顺序贝叶斯学习自适应地更新降解过程的关键控制参数。进一步提出了一种高效的插值算法来减轻 RUL 分布的计算负担。对涡扇发动机退化数据和现场列车轴承振动数据进行的案例研究表明，与现有方法相比，模型性能更胜一筹。