半导体制造中不确定性与业务感知的剩余使用寿命估计
Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
- University of Padova（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出结合深度学习序列模型与同时分位数回归的预测性维护框架，用于半导体制造中不确定性感知的剩余使用寿命估计，其中S4D模型在PHM18数据上表现最佳，显著降低业务成本。
AI中文摘要：
半导体制造依赖于紧密互联的组件，因此及早识别最可能失效的资产对于防止单个故障中断整个生产线至关重要。因此，维护规划必须在意外故障与过早中断运行寿命之间取得平衡。我们提出了一种预测性维护（PdM）框架，该框架结合了深度学习（DL）序列模型和同时分位数回归（SQR），用于不确定性感知的剩余使用寿命（RUL）估计和风险感知的维护决策。我们在2018年PHM数据挑战赛（PHM18）的离子铣削数据上比较了几种架构，包括基于状态空间模型（SSM）的架构，并使用预测和业务指标进行评估：意外中断（UB）、未利用寿命（UL）以及成本加权目标。对角状态空间（S4D）在各个分位数上提供了最佳的剩余使用寿命（RUL）估计，并且相对于预防性维护（PvM）基线，通过避免系统性的早期干预，大幅降低了业务成本。结果支持半导体生产中不确定性感知、成本敏感的维护规划。
英文摘要：
Semiconductor manufacturing relies on tightly interconnected components, so early identification of the assets most likely to fail is essential to prevent a single breakdown from disrupting the entire production pipeline. Maintenance planning must therefore balance unexpected failures against prematurely interrupted operating life. We present a Predictive Maintenance (PdM) framework combining Deep Learning (DL) sequence models and Simoultaneous Quantile Regression (SQR) for uncertainty-aware Remaining Useful Life (RUL) estimation and risk-aware maintenance decisions. Several architectures are compared on ion-milling data from the 2018 PHM Data Challenge (PHM18), including architectures based on State Space Models (SSM), using prediction and business metrics: Unexpected Breaks (UB), Unexploited Lifetime (UL), and a cost-weighted objective. Diagonal State Spaces (S4D) delivers the best Remaining Useful Life (RUL) estimates across quantiles and, relative to Preventive Maintenance (PvM) baselines, substantially lowers business cost by avoiding systematically early interventions. The results support uncertainty-aware, cost-sensitive maintenance planning in semiconductor production.
