Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement

Khouloud Abdelli; Helmut Griesser; Stephan Pachnicke

doi:10.1109/ICTON51198.2020.9203551

Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement

Signal Processing 2022-03-24 v1 Machine Learning

Authors: Khouloud Abdelli , Helmut Griesser , Stephan Pachnicke

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Abstract

In this paper, a data-driven diagnostic and prognostic approach based on machine learning is proposed to detect laser failure modes and to predict the remaining useful life (RUL) of a laser during its operation. We present an architecture of the proposed cognitive predictive maintenance framework and demonstrate its effectiveness using synthetic data.

Keywords

machine learning fault detection and diagnosis deep learning for detection and classification

Cite

@article{arxiv.2203.11728,
  title  = {Machine Learning based Data Driven Diagnostic and Prognostic Approach for Laser Reliability Enhancement},
  author = {Khouloud Abdelli and Helmut Griesser and Stephan Pachnicke},
  journal= {arXiv preprint arXiv:2203.11728},
  year   = {2022}
}

Comments

2020 22nd International Conference on Transparent Optical Networks (ICTON)

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