发表机构
Brandenburg University of Technology Cottbus-Senftenberg（勃兰登堡工业大学科特布斯-森夫滕贝格分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TREDD方法，通过基于趋势的参考评估检测退化，结合平滑、基线估计和漂移检测，在NASA电池数据集上验证，支持可解释的置信度评估。
AI 中文摘要
技术系统越来越多地通过传感器和运行数据进行监测，以便及早检测退化与性能劣化。然而，测量数据中观察到的趋势不一定对应于物理老化，因为噪声、离群值、不稳定的初始区域或变化的运行条件可能产生相似的模式。本文提出基于趋势的参考评估用于退化检测（TREND-based Reference Evaluation for Degradation Detection，TREDD），这是一种可解释的方法，用于将退化检测为相对于早期参考状态的持续、基于趋势的偏差。TREDD结合了滚动窗口平滑、基线估计、方向相关的退化指数、长期趋势提取和持续漂移检测。此外，该方法通过纳入数据质量评估、上下文检查和鲁棒辅助分析，将计算漂移检测与将漂移解释为合理的物理老化分离开来。该方法在NASA锂离子电池老化数据集上进行了评估，使用放电循环容量作为与退化相关的条件变量。代表性的案例研究表明，TREDD能够识别清晰的退化轨迹，同时对微弱、渐进或非典型趋势赋予较低的置信度。因此，该方法支持透明且基于置信度的退化解释，而非纯粹预测性的电池健康估计。
英文摘要
Technical systems are increasingly monitored using sensor and operational data to detect degradation and performance deterioration at an early stage. However, observed trends in measurement data do not necessarily correspond to physical aging, since noise, outliers, unstable initial regions, or changing operating conditions may produce similar patterns. This paper proposes Trend-based Reference Evaluation for Degradation Detection (TREDD), an interpretable method for detecting degradation as a persistent, trend-based deviation from an early reference state. TREDD combines rolling-window smoothing, baseline estimation, a direction-dependent degradation index, long-term trend extraction, and persistent drift detection. In addition, the method separates computational drift detection from the interpretation of drift as plausible physical aging by incorporating data-quality assessment, context checking, and robust auxiliary analysis. The approach is evaluated on the NASA Lithium-Ion Battery Aging Dataset using discharge-cycle capacity as the degradation-relevant condition variable. The representative case study illustrates that TREDD can identify clear degradation trajectories while assigning reduced confidence to weak, gradual, or atypical trends. The method therefore supports transparent and confidence-based degradation interpretation rather than purely predictive battery health estimation.
Comments7 Pages, 2 figures, Conference: IECON 2026 (18 to 21 October 2026)
