Rethinking RUL Prediction Uncertainty, Robustness, Interpretability, and Feasibility Matter
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Abstract
Prognostics and Health Management (PHM) plays a key role in predicting the Remaining Useful Life (RUL) of systems, which is essential for enabling decision-making for Predictive Maintenance (PdM) and operations. While most research has traditionally focused on improving the accuracy of RUL predictions, this paper argues that four essential characteristics, uncertainty, robustness, interpretability, and feasibility, are key for real-world PHM applications. This study explores these characteristics through a comparative analysis of two data-driven models (DDMs): the probabilistic Bidirectional Long Short-Term Memory (BiLSTM) model and the Adaptive Hidden Semi-Markov Model (AHSMM). Deep Learning (DL) models such as the BiLSTM often achieve high prediction accuracy but struggle with uncertainty quantification and adaptability across varying operating conditions. In contrast, stochastic models like AHSMM offer stronger robustness and feasibility, performing well even with limited or noisy data. Using the C-MAPSS dataset, the models are evaluated through the lens of the four proposed characteristics. This more holistic approach clarifies each model’s strengths, limitations, and practical trade-offs in PHM settings. The findings highlight that while accuracy remains important, focusing solely on it can overlook critical factors that affect model performance in real operational environments. Balancing all four characteristics is essential for deploying reliable and effective decision-making for predictive maintenance and operations.
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remaining useful life, robustness, interpretability, uncertainty, feasibility, data-driven models
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