Similarity-based prediction modeling is a common method for estimating the remaining useful life (RUL) of a machine. The present study proposes a novel similarity-based multidimensional sequential approach to enhance the prediction accuracy of engine remaining useful life. The proposed approach involves the following steps: Initially, a screening of degradation-sensitive sensor variables is conducted through trend intensity analysis of all sensor data. Subsequently, historical degradation trajectories are constructed through polynomial fitting on selected sensor sequences. Then, multidimensional simultaneous sliding similarity matching is implemented between test samples and historical trajectories, with Manhattan distance summation serving as the similarity metric. Finally, the most similar historical trajectory segments are selected, and RUL reference values are derived from the most similar historical trajectories. Weighted RUL estimates are then calculated based on similarity levels. The validation using the C-MAPSS dataset demonstrates the efficacy of the method, with a root mean square error (RMSE) of 15.14 and a score function value of 334.18, thereby surpassing other methods in terms of computational simplicity and prediction accuracy.
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