Lifetime Prediction of 1550 nm DFB Laser using Machine learning Techniques

Abdelli, Khouloud; Rafique, Danish; Grieser, Helmut; Pachnicke, Stephan

doi:10.1364/ofc.2020.th2a.3

Cited by 6 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate and repeatable extrapolation results were obtained in S-on-1 laser-induced damage fatigue experiments. Besides, in [55], an ANN-based lifetime prediction of 1550 nm DFB laser was presented to output the possible mean-timeto-failure, which improved prediction error from 3.8-17 years to 1.12 years. Based on synthetic data and real laser datasheet including different operating conditions, this scheme effectively reduced the time and cost of aging tests, as summarized in Table 2.…”

Section: Failure Predictionmentioning

confidence: 99%

A review of machine learning-based failure management in optical networks

Wang

Zhang²,

Chen³

et al. 2022

Sci. China Inf. Sci.

View full text Add to dashboard Cite

Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for performing comprehensive data analysis and complex network management and is widely utilized for failure management in optical networks to revolutionize the conventional manual methods. In this study, the background of failure management is introduced, where typical failure tasks, physical objects, ML algorithms, data sources, and extracted information are illustrated in detail. An overview of the applications of ML in failure management is provided in terms of alarm analysis, failure prediction, failure detection, failure localization, and failure identification. Finally, the future directions on ML for failure management are discussed from the perspective of data, model, task, and emerging techniques.

show abstract

Section: Failure Predictionmentioning

confidence: 99%

A review of machine learning-based failure management in optical networks

Wang

Zhang²,

Chen³

et al. 2022

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…reliability metric) as a function of different laser degradation parameters. See [4] for more details on the process of synthetic reliability data generation. The correlation matrix shown in Figure 2 yielded that the two most important parameters influencing the laser reliability are junction temperature and the current.…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%