基于物理引导域适应的变工况下飞机压缩机实际剩余使用寿命预测
Towards Real-World RUL Prediction for Aircraft Compressors Under Variable Conditions with Physics-Guided Domain Adaptation
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中文总结 AI 辅助
针对飞机压缩机在实际运行中剩余使用寿命预测的难题，提出结合物理引导处理与自适应时间编码的框架，确定喘振裕度为退化指标，经多种处理实现跨单元比较和跨域精度提升，还能提供不确定性估计以支持维护调度。
中文摘要 AI 辅助
在实际商业运行中，飞机离心式空气压缩机的剩余使用寿命预测面临诸多挑战，如受控基准数据集未涵盖的问题。飞行中的传感器信号在不断变化的运行条件下叠加了真实的退化，且无法预先假定某个通道能携带可靠的退化特征。此外，在机队的一个子集上训练的模型对未见过的飞机和安装位置泛化性差。本文提出了一个将物理引导处理与自适应时间编码相结合的框架。通过总体水平选择程序确定喘振裕度是整个机队中最一致的退化指标。运行状态过滤和窗口聚合从运行噪声中恢复健康趋势，两个基于物理的特征编码当前健康水平和累积退化率，无需绝对信号值即可进行跨单元比较。正弦位置编码提供生命周期上下文且无数据泄露，结构化跨域评估确定编码周期不匹配是机队转移下性能损失的主要机制。自适应重新校准方案仅从早期观测中估计每个目标单元的生命周期尺度，无需未来信息或标记的目标数据。该方法在跨域距离不断增加的转移场景中，跨域精度有显著且一致的提高，高斯过程回归模型还提供了校准的不确定性估计，支持基于风险的维护调度。
英文摘要
Remaining useful life prediction for aircraft centrifugal air compressors in real commercial operations poses challenges that controlled benchmark datasets do not expose. In-flight sensor signals superimpose genuine degradation on continuously varying operating conditions, and no channel can be assumed a priori to carry a reliable degradation signature. Moreover, models trained on one subset of a fleet generalize poorly to unseen aircraft and installation positions, a cross-domain problem of underappreciated practical severity. This paper presents a framework combining physics-guided processing with adaptive temporal encoding. A population-level selection procedure identifies surge margin as the most consistent degradation indicator across the fleet. Operating regime filtering and windowed aggregation recover the health trend from operational noise, and two physically motivated features encoding current health level and cumulative degradation rate enable cross-unit comparison without absolute signal values. Sinusoidal positional encoding provides lifecycle context without data leakage, and a structured cross-domain evaluation identifies encoding period mismatch as the primary mechanism of performance loss under fleet transfer. An adaptive recalibration scheme estimates each target unit's lifecycle scale from early observations alone, requiring no future information or labeled target data. The approach yields substantial, consistent gains in cross-domain accuracy across transfer scenarios of increasing domain distance, and a Gaussian Process Regression model further provides calibrated uncertainty estimates supporting risk-informed maintenance scheduling.
发表机构
- University of Connecticut（康涅狄格大学）
- RTX-Collins Aerospace（RTX柯林斯航空航天公司）
机构由 AI 辅助整理，请以论文原文为准。
