Improvement by Monte Carlo for Trajectory Similarity-Based RUL Prediction

Trajectory similarity-based evaluation is the most intuitive method for remaining useful life (RUL) prediction when abundant run-to-failure data are available. However, practical scenarios often present a challenge with limited access to such trajectories. This article introduces an improved similarity-based method to bridge the gap by employing Monte Carlo to generate more trajectories. To begin, 13 features are extracted from both time and frequency domains, subsequently conducting dimension reduction to build the health index (HI) corresponding to the original acceleration measurements. Afterward, HI trajectories are fit using exponential functions, and three different distribution functions (Gaussian, Gamma, Weibull) are adopted to identify the probability density of the two coefficients (
$\theta $
,
$\beta $
) in fit exponential models. Monte Carlo is applied to resample from the coefficient distribution, thereby generating more HI trajectories. Finally, the expanded HI library is used for RUL prediction and validated with two different bearing datasets. Experimental findings reveal that the trajectory expansion achieved through Monte Carlo sampling yields more accurate RUL estimation and reduced uncertainty.