从绕组故障几何到可靠性：定子匝间故障的估计与预测
From Winding-Fault Geometry to Reliability: Estimation and Prognosis of Stator Inter-Turn Faults
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中文总结 AI 辅助
本文提出一个集成框架，利用增广状态粒子滤波器估计感应电机定子匝间故障，结合四种预测模型进行剩余寿命预测与可靠性评估，实现从故障建模到诊断预测的统一连接。
中文摘要 AI 辅助
本文开发了一个用于感应电机定子匝间故障的估计、预测和可靠性评估的集成框架。该故障由其严重程度（定义为短路匝数比例）及其空间方向来表征。一个几何故障模型表明，由此产生的输出特征在故障严重程度上是仿射的，并表现出基本的空间周期性。利用这一结构，一个增广状态粒子滤波器联合估计非线性机电状态和未知的故障严重程度，同时量化后验不确定性。然后，使用四种预测模型传播估计的退化过程：线性趋势、霍尔特指数平滑、贝叶斯退化和基于粒子的预测。它们的预测与阈值交叉剩余使用寿命（RUL）、首达可靠性、退化相关风险可靠性和威布尔寿命基准相关联，从而提供确定性和概率性的健康评估。数值结果表明，能够准确进行在线故障估计和输出重构，表征退化模式和预测范围对预测的影响，并显示一致可靠性和阈值交叉预测。此外，在空间周期性引起的不同故障方向下，估计精度保持相当。所提出的框架提供了从基于物理的匝间故障建模到在线诊断、退化预测和可靠性评估的统一连接。
英文摘要
This paper develops an integrated framework for estimation, prognosis, and reliability assessment of stator inter-turn faults in induction motors. The fault is characterized by its severity, defined as the fraction of short-circuited turns, and its spatial orientation. A geometric fault model shows that the resulting output signature is affine in the fault severity and exhibits a fundamental spatial periodicity. Exploiting this structure, an augmented-state particle filter jointly estimates the nonlinear electromechanical state and the unknown fault severity while quantifying posterior uncertainty. The estimated degradation is then propagated using four prognostic models: linear trend, Holt exponential smoothing, Bayesian degradation, and particle-based forecasting. Their predictions are connected to threshold-crossing remaining useful life (RUL), first-passage reliability, degradation-dependent hazard reliability, and a Weibull lifetime benchmark, thereby providing both deterministic and probabilistic health assessments. Numerical results demonstrate accurate online fault estimation and output reconstruction, characterize the effects of degradation pattern and prediction horizon on prognosis, and show consistent reliability and threshold-crossing predictions. Moreover, estimation accuracy remains comparable across the distinct fault orientations induced by the spatial periodicity. The resulting framework provides a unified connection from physics-based inter-turn fault modeling to online diagnosis, degradation prognosis, and reliability assessment.
