What question did this study set out to answer?

The aim is to determine the optimal configuration of convolutional and LSTM layers for predicting the remaining useful life of aero-engines.

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May 22, 2021

Remaining Useful Life Prediction for Aero-Engine Based on LSTM and CNN

Key Points

The aim is to determine the optimal configuration of convolutional and LSTM layers for predicting the remaining useful life of aero-engines.
Developed separate models for CNN and LSTM to identify optimal layer counts.
Constructed a combined CNN-LSTM model for improved accuracy.
Conducted experiments using the C-MAPSS dataset for RUL predictions.
LSTM with 2 dense layers achieved the highest prediction accuracy among all tested models.
Depth of convolutional and LSTM layers significantly impacts network performance.

Abstract

Data-driven Remaining Useful Life (RUL) prediction for aero-engine has evolved rapidly in recent years. Especially, deep learning-based methods like Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) have achieved excellent results. However, there is still limited study to identify the effect on network performance from the number of convolutional layers, LSTM layers and their combination structure. Therefore, the optimal number of convolutional layers and LSTM layers was first determined for CNN and LSTM respectively in this paper. A combined network CNN-LSTM was then constructed. Three kinds of deep networks (CNN, LSTM and CNN-LSTM) were compared on aero-engine RUL prediction. Experimental results on the C-MAPSS dataset indicated that LSTM with 2 dense layers achieved the highest prediction accuracy.

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Cite This Study

Ruan et al. (Sat,) studied this question.

synapsesocial.com/papers/6a10342728c2d29469fe5b4e https://doi.org/https://doi.org/10.1109/ccdc52312.2021.9601773

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