Abstract
Recent research increasingly integrates machine learning (ML) into predictive maintenance (PdM) to reduce operational and maintenance costs in data-rich operational settings. However, uncertainty due to model misspecification continues to limit widespread industrial adoption. This paper proposes a PdM framework in which sensor-driven prognostics inform decision-making under economic trade-offs within a finite decision space. We investigate two key questions: (1) Does higher predictive accuracy necessarily lead to better maintenance decisions? (2) If not, how can the impact of prediction errors on downstream maintenance decisions be mitigated? We first demonstrate that in the traditional estimate-then-optimize (ETO) framework, errors in probabilistic prediction can result in inconsistent and suboptimal maintenance decisions. To address this, we propose an integrated estimate-optimize (IEO) framework that jointly tunes predictive models while directly optimizing for maintenance outcomes. We establish theoretical finite-sample guarantees on decision consistency under standard assumptions. Specifically, we develop a stochastic perturbation gradient descent algorithm suitable for small run-to-failure datasets. Empirical evaluations on a turbofan maintenance case study show that the IEO framework reduces average maintenance regret up to 22% compared to ETO. This study provides a principled approach to managing prediction errors in data-driven PdM. By aligning prognostic model training with maintenance objectives, the IEO framework improves robustness under model misspecification and improves decision quality. The improvement is particularly pronounced when the decision-making policy is misaligned with the decision-maker's target. These findings support more reliable maintenance planning in uncertain operational environments.
Abstract (translated)
最近的研究越来越多地将机器学习（ML）融入预测性维护（PdM），以减少数据密集型操作环境中的运营和维护成本。然而，由于模型规格不匹配导致的不确定性继续限制了其在工业领域的广泛应用。本文提出了一种基于传感器驱动预测的PdM框架，在该框架中，经济权衡下的决策制定在一个有限的决策空间内进行。我们探讨了两个关键问题：（1）更高的预测准确性是否必然会导致更好的维护决策？（2）如果不尽然，如何减轻预测误差对后续维护决策的影响？ 首先，我们展示了在传统的估计然后优化（ETO）框架中，概率预测中的错误可能导致不一致和次优的维护决策。为了解决这个问题，我们提出了一个集成估计-优化（IEO）框架，该框架联合调优预测模型的同时直接针对维护结果进行最优化。我们在标准假设下建立了理论上的有限样本保证，以确保决策的一致性。具体来说，我们开发了一种适合于小规模运行至故障数据集的随机扰动梯度下降算法。 在对涡轮风扇维护案例的研究中，实证评估表明IEO框架相比于ETO减少了高达22%的平均维护遗憾（即未达到最佳维护效果的程度）。这项研究提供了一个基于数据驱动PdM管理预测误差的原则性方法。通过将诊断模型训练与维护目标相结合，IEO框架在模型规格不匹配的情况下提高了鲁棒性和决策质量。这种改进尤其明显于当决策策略与决策者的目标不一致时。 这些发现支持了在不确定的操作环境中进行更可靠的维护规划。
URL
https://arxiv.org/abs/2506.19698
