Stacking-based ensemble learning for remaining useful life estimation

Abstract

Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

“…turbofan engines [6] [7] [9]. In this work, we proposed a novel RUL prediction approach of combining optimal noise injection and Kalman filters-based bagging.…”

“…The stacking ensemble learning method combines four learning methods with large differences, deep neural networks, support vector machine, extreme gradient boosting, and k-nearest neighbors, and thus achieves better RUL prediction results. A stacking-based ensemble learning method that combines five regression algorithms (linear regression, support vector machine, decision tree, random forest, and extreme gradient boosting) was developed to increase RUL prediction performance on NASA's turbofan engine degradation datasets [9]. An optimized random forest model was proposed to obtain the underlying mapping relationship between the aging features and capacity, then RUL predictions of li-ion batteries were achieved [10].…”