Nov 1, 2019 · In this paper, we proposed a novel RUL prediction method by using complete ensemble empirical mode decomposition (CE-EMD) for trend feature construction and long …

Mar 1, 2022 · To break through the limitation of prediction accuracy under variable operating conditions, this paper presents a novel multi-dimensional recurrent neural network (MDRNN) …

Feb 28, 2020 · A systematic prognostics framework named recurrent convolutional neural network is proposed for RUL prediction of machinery, and its effectiveness and superiority are verified …

Jul 12, 2021 · Recently, different deep learning (DL) techniques have been used for RUL prediction and achieved great success. Because the data is often time-sequential, recurrent …

In this paper, we propose to use transfer learning-based deep neural networks for RUL prediction. In recent years, transfer learning has made great progress in image, audio, and text …

To solve above problems, a novel RUL prediction approach based on eXtreme gradient boosting (XGBoost) and multi-quantile recursive neural network (MQ-RNN) is proposed in this article. …

The back of the model is the recurrent convolution layer to model the temporal dependence relationship under different degradation features. Normalized life values are used as labels to …

Dec 1, 2021 · This paper systematically reviews RNN and its variants for RUL prediction, with a specific focus on understanding how different components (e.g., types of optimisers and …

Remaining useful life (RUL) prediction is an engineer-ing discipline that works on the prediction of the future state or response of a given system based on synthesis observations, calibrated …

This machine learning algorithm is one of the hot spots and key points in the current research algorithms of equipment life prediction ... Bearings 1_2, 1_3, and 1_5 test and verify the …