Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep learning. This includes the state-of-the-art variational inference algorithms for Bayesian neural networks (BNN) as well as popular alternatives such as Monte Carlo Dropout (MCD), deep ensembles (DE) and heteroscedastic neural networks (HNN). All the inference techniques share the same inception deep learning architecture as a functional model. We performed hyperparameter search to optimize the main variational and learning parameters of the algorithms. The performance of the methods is evaluated on a subset of the large NASA NCMAPSS dataset for aircraft engines. The assessment includes RUL prediction accuracy, the quality of predictive uncertainty, and the possibility to break down the total predictive uncertainty into its aleatoric and epistemic parts. The results show no method clearly outperforms the others in all the situations. Although all methods are close in terms of accuracy, we find differences in the way they estimate uncertainty. Thus, DE and MCD generally provide more conservative predictive uncertainty than BNN. Surprisingly, HNN can achieve strong results without the added training complexity and extra parameters of the BNN. For tasks like active learning where a separation of epistemic and aleatoric uncertainty is required, radial BNN and MCD seem the best options.

翻译：在预测性维护中，对剩余使用寿命预测进行可靠的不确定性量化对于做出明智决策至关重要。为此，我们评估了预后深度学习领域不确定性量化的最新进展，包括贝叶斯神经网络（BNN）最先进的变分推理算法，以及蒙特卡洛丢弃法（MCD）、深度集成（DE）和异方差神经网络（HNN）等主流替代方案。所有推理技术均采用相同的初始深度学习架构作为功能模型。我们通过超参数搜索优化了算法的主要变分参数和学习参数。基于大型NASA NCMAPSS飞机发动机数据集的子集，对方法性能进行了评估，涵盖剩余使用寿命预测精度、预测不确定性质量，以及将总预测不确定性分解为偶然不确定性与认知不确定性的可行性。结果表明，没有任何方法在所有情境下均具备明显优势。尽管各方法在精度上表现接近，但其不确定性估计方式存在差异。与贝叶斯神经网络相比，深度集成和蒙特卡洛丢弃法通常提供更保守的预测不确定性。令人惊讶的是，异方差神经网络无需贝叶斯神经网络的额外训练复杂度与参数即可取得优异结果。在需要分离认知不确定性与偶然不确定性的主动学习等任务中，径向贝叶斯神经网络与蒙特卡洛丢弃法表现最佳。