A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction

TL;DR

This paper introduces a hybrid CNN-LSTM model designed to accurately predict the remaining useful life of lasers, demonstrating superior performance over traditional approaches.

Contribution

The paper presents a novel hybrid CNN-LSTM model specifically tailored for laser RUL prediction, improving accuracy over existing methods.

Findings

Outperforms conventional RUL prediction methods

Demonstrates higher accuracy in laser RUL estimation

Validates effectiveness through experimental results

Abstract

A hybrid prognostic model based on convolutional neural networks (CNN) and long short-term memory (LSTM) is proposed to predict the laser remaining useful life (RUL). The experimental results show that it outperforms the conventional methods.