Implementation of optical module performance prediction and maintenance on data-driven

Li, Dongmei; Yang, Yongjun; Tang, Zhifei; He, Zheng

doi:10.1117/12.2627880

Cited by 3 publications

(1 citation statement)

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“…Optical modules encounter several types of failure, including launch power degradation, bias current anomaly, temperature rise, laser anomaly, wavelength drift, signal performance imperfection, and receiving sensitivity deterioration. As a packaged active component, failure check and troubleshooting of the optical module is always challenging and is conventionally performed by experienced engineers [13].…”

Section: Managed Objects In Optical Network and Typical Failure Categ...mentioning

confidence: 99%

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

Wang

Zhang²,

Chen³

et al. 2022

Sci. China Inf. Sci.

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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.

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Section: Managed Objects In Optical Network and Typical Failure Categ...mentioning

confidence: 99%