Novel Segmentation Methodology for Robust Feature Engineering of Time Series Data in Prognostics and Health Management

Time series segmentation plays a critical role in feature engineering for prognostics and health management (PHM), yet most existing approaches rely on domain-specific rules or fail to preserve meaningful transient patterns. This research proposes a segmentation-driven framework that leverages a greedy Perceptually Important Point (PIP) algorithm to identify informative structural regimes in sensor signals without prior domain knowledge. A global reference signal is constructed from class-level Euclidean-barycenter averages, and consistent segment boundaries are applied across all samples. Segment-level statistical features are then extracted and used for classification. Evaluation on a chemical gas sensor dataset demonstrates that the proposed method significantly outperforms traditional whole-signal summary statistics, achieving improved robustness to drift and unit variability. Future work includes parameter optimization of the PIP algorithm, exploration of class-sensitive segmentation strategies, and extension of the framework to remaining useful life (RUL) prediction and anomaly detection tasks.