TREDD: Robust Trend-Based Reference Evaluation for Interpretable Degradation Detection

Abstract

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.