A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method

TL;DR

This paper introduces a novel RUL prediction method combining trend feature construction via ensemble empirical mode decomposition and a neural network model, improving accuracy over traditional fixed-window approaches.

Contribution

It proposes a new trend feature extraction method for RUL prediction and integrates it with an LSTM model, enhancing prediction accuracy across datasets.

Findings

Achieves the lowest root mean square error in RUL prediction on C-MAPSS dataset.

Outperforms existing state-of-the-art methods in RUL prediction accuracy.

Demonstrates robustness of the trend feature approach across different degradation patterns.

Abstract

Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features. This paper proposes a method for RUL prediction which depends on a trend feature representing the overall time sequence of degradation. Complete ensemble empirical mode decomposition, followed by a reconstruction procedure, is created to build the trend features. The probability distribution of sensors' measurement learned by conditional neural processes is used to evaluate the trend features. With the best trend feature, a data-driven model using long short-term memory is developed to predict the RUL. To prove the effectiveness of the proposed method, experiments on a benchmark C-MAPSS dataset are carried out and compared with other state-of-the-art methods. Comparison results show that the proposed method achieves the smallest root mean square values in prediction of all RUL.