Novel Segmentation Methodology for Robust Feature Engineering of Time Series Data in Prognostics and Health Management
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Abstract
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.
How to Cite
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Time series segmentation, feature engineering, time series data
Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.
Olszewski, R. T. (2001). Generalized feature extraction for structural pattern recognition in time-series data. Carnegie Mellon University.
Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Computation, 16(8), 1661–1687.
Srinivasan, B., & Rengaswamy, R. (2012). Automatic oscillation detection and characterization in closed-loop systems. Control Engineering Practice, 20(8), 733–746.
Wilson, S. J. (2017). Data representation for time series data mining: Time domain approaches. Wiley Interdisciplinary Reviews: Computational Statistics, 9(1), e1392.
Yang, S. (2016). An adaptive prognostic methodology and system framework for engineering systems under dynamic working regimes [PhD Thesis]. University of Cincinnati.
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