January 1, 2025Open Access

A Data-Driven Particle Filter Approach for System-Level Prediction of Remaining Useful Life

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The data-driven method improves the estimation of remaining useful life in industrial systems, enhancing predictive maintenance efforts.
Key characteristics adapt over time, improving the model's performance influenced by past predictive performance.
The approach models degradation as a stochastic process, bypassing traditional state-space modeling and utilizing performance metrics.
High-fidelity degradation signals from a UAV simulation dataset validate predictive accuracy against observed performance metrics.

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This approach predicts remaining useful life in industrial systems, indicating enhanced predictive maintenance strategies through particle filtering.

Abel et al. (2025) studied this question.

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This approach predicts remaining useful life in industrial systems, indicating enhanced predictive maintenance strategies through particle filtering.