Abstract The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.

中文翻译：

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通过贝叶斯神经网络和哈密顿蒙特卡罗在剩余使用寿命估计中利用不确定性信息

摘要 机械剩余使用寿命（RUL）的估计是预测和健康管理（PHM）中的一项主要任务。最近，由于深度学习 (DL) 模型的应用，预测性能得到了显着提高。然而，只有少数作者评估应用的 DL 模型的不确定性，因此可以说明模型对预测 RUL 值的确定程度。这在应用程序中尤其重要，在这些应用程序中，计划外故障会导致高成本甚至人员伤害。因此，确定与 RUL 估计相关的不确定性对于 DL 模型在实践中的适用性很重要。在本文中，自然量化不确定性的贝叶斯 DL 模型被应用于模拟涡轮风扇发动机的 RUL 估计任务。推理是通过哈密顿蒙特卡罗 (HMC) 和变分推理 (VI) 进行的。实验表明，与经典 DL 模型相比，贝叶斯 DL 模型的性能相似，并且在许多情况下甚至是有益的。此外，还提出了一种利用贝叶斯 DL 模型生成的不确定性信息的方法。该方法被应用并展示了如何进一步提高预测性能。