Combining first prediction time identification and time-series feature window for remaining useful life prediction of rolling bearings with limited data

Li, Hai; Wang, Chaoqun

doi:10.1177/1748006x221147441

Cited by 5 publications

(1 citation statement)

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“…Kong et al [62] developed a first prediction time (FPT) identification method, combined with degeneracy factors and SBiLSTM, and proposed a multi-step ahead rolling prediction method to predict RUL. Li et al [39] used first prediction time identification and time-series feature windows to predict the RUL of rolling bearings under limited data. Zhu et al [40] proposed a Bayesian semi-supervised transfer learning intelligent fault prediction framework based on active query, which can perform RUL prediction across machines under limited data.…”

Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction via a Data-Driven Deep Learning Fusion Model-CALAP

Wu,

Ye,

et al. 2023

IEEE Access

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As one of the key technologies in the field of Prognostic and Health Management (PHM), Remaining Useful Life (RUL) prediction technology plays an important role in equipment health maintenance and fault detection. For complex devices, the degradation process of the remaining useful life of the device is often difficult to be described with mathematical or physical models, so data-driven methods has become an important and feasible method in the field of RUL prediction. This paper proposes a datadriven deep learning fusion model based on CNN-ATTENTION-LSTM-ATTENTION-PARALLEL (CALAP) model, in which Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are adopted to extract the spatial features and temporal features of the data in parallel, and both of them combine the corresponding attention mechanism allowing the network to focus on important factors. The CNN path fuses CBAM and the LSTM path fuses attention mechanism. We evaluate the proposed model on the C-MAPSS dataset released by NASA and compare it to the state-of-the-art. The RMSE of the proposed model is reduced by nearly 2-5%, and the score is reduced by nearly 8-10% under simple conditions. Experimental results prove that the model has relatively high prediction accuracy and good robustness.

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Section: Literature Reviewmentioning

confidence: 99%