从噪声到预后：一种基于物理的分数域框架用于航空传动系统早期齿轮故障检测
From Noise to Prognosis: A Physics-Grounded, Fractional-Domain Framework for Early Gear Fault Detection in Aviation Drivetrains
AI总结：
本文提出LDME框架，结合双路径去噪、多尺度分解、分数域增强和统计异常评分，实现无监督的故障检测与预测，通过CWRU、HUMS 2023和模拟数据验证其在不同工况下的有效性。
摘要：
早期和可靠的齿轮故障检测对于航空安全和运行可用性至关重要。我们提出了局部损伤模式提取器（LDME），一种结构化、基于物理的信号处理框架，结合双路径去噪、多尺度分解、分数域增强和统计原理的异常评分，以生成可解释的条件指标。LDME分为三层：（i）双路径去噪（DWT结合自适应Savitzky-Golay平滑）以抑制宽带噪声并保留瞬态故障结构；（ii）多尺度损伤增强使用Teager-Kaiser前置放大器后接Hadamard-Caputo分数算子以强调非正弦低频故障特征；（iii）决策融合，其中谐波感知的傅里叶指标通过无监督异常检测器结合并评分。使用Case Western Reserve University（CWRU）轴承数据集、HUMS 2023行星齿轮箱基准和受控模拟数据集的评估表明，LDME在各种工况下一致地区分正常、早期裂纹和传播裂纹阶段。LDME比HT-TSA早198个周期识别主要检测事件，并将维护建议时间从383个周期缩短到365个周期。我们讨论了其与先前艺术的关系、限制和未来理论方向。所有代码和实验配置均记录以确保可重复性。
英文摘要：
Early and reliable detection of gear faults in complex drivetrain systems is critical for aviation safety and operational availability. We present the Local Damage Mode Extractor (LDME), a structured, physics-informed signal processing framework that combines dual-path denoising, multiscale decomposition, fractional-domain enhancement, and statistically principled anomaly scoring to produce interpretable condition indicators without supervision. LDME is organized in three layers: (i) dual-path denoising (DWT with adaptive Savitzky-Golay smoothing) to suppress broadband noise while preserving transient fault structure; (ii) multi-scale damage enhancement using a Teager-Kaiser pre-amplifier followed by a Hadamard-Caputo fractional operator that accentuates non-sinusoidal, low-frequency fault signatures; and (iii) decision fusion, where harmonics-aware Fourier indicators are combined and scored by an unsupervised anomaly detector. Evaluation using the Case Western Reserve University (CWRU) bearing dataset, the HUMS 2023 planetary gearbox benchmark, and a controlled simulated dataset shows that LDME consistently distinguishes nominal, early-crack, and propagated-crack stages under various operating conditions. LDME identifies the primary detection event earlier (198 cycles) than HT-TSA (284 cycles) and advances maintenance recommendation time from 383 to 365 cycles. We discuss its relation to prior art, limitations, and future theoretical directions. All code and experimental configurations are documented for reproducibility.
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