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

Khouloud Abdelli; Khouloud Abdelli; Helmut Grießer; Stephan Pachnicke

doi:10.1364/OECC.2021.S3D.3

26th Optoelectronics and Communications Conference
OSA Technical Digest (Optica Publishing Group, 2021),
paper S3D.3
•https://doi.org/10.1364/OECC.2021.S3D.3

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

Khouloud Abdelli, Helmut Grießer, and Stephan Pachnicke

Optoelectronics and Communications Conference 2021

Hong Kong China
3–7 July 2021
ISBN: 978-1-943580-92-7

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Poster Session IV: Optical Devices and Modules (S3D)

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K. Abdelli, H. Grießer, and S. Pachnicke, "A Hybrid CNN-LSTM Approach for Laser Remaining Useful Life Prediction," in 26th Optoelectronics and Communications Conference, P. Alexander Wai, H. Tam, and C. Yu, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper S3D.3.

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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.

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