Demonstration of a Novel Framework for Proactive Maintenance Using Failure Prediction and Bit Lossless Protection With Autonomous Network Diagnosis System

Inuzuka, Fumikazu; Oda, Takuya; Tanaka, Takafumi; Kitamura, Keiichi; Kuwabara, Seiki; Hirano, A.; Tomizawa, M.

doi:10.1109/jlt.2020.2967510

Cited by 14 publications

(2 citation statements)

References 11 publications

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“…In [48], a Naïve Bayes was used for BER degradation prediction based on real-time state-of-polarization (SOP) monitoring with low complexity, minimal storage, and high speed. In [49], a framework of proactive maintenance was designed for fiber diagnosis. Here, a CNN was adopted to estimate the bend state of remote fiber; protection switching was executed as soon as fiber degradation was detected with no service disruption or bit loss.…”

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.

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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: 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.

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show abstract

“…If the failure of communication system can be accurately predicted in time, the maintenance and the repair work can be done well. It would be changed from "after-the-fact maintenance" to "on-demand maintenance", from "scheduled maintenance" to "at any time maintenance", from "repair maintenance" to "preventive maintenance", which can not only improve the battlefield maintainability and availability of communication system and avoid the failure of combat operations caused by the system failure, but can also save maintenance costs and effectively improve the efficiency-cost ratio of communication system maintenance [1][2][3] .…”

Section: Introductionmentioning

confidence: 99%

Study on communication system fault prediction based on multivariate grey model

Xu,

Zhou,

Wang

et al. 2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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Communication system failures often occur and it is difficult to find a certain pattern, this paper addresses the difficulty of fault prediction in electronic information systems represented by communication systems. Considering the characteristics of communication systems, such as complex structure, high integration, working principle and fault mechanism, this paper proposes to use the Multivariate Growth Model (MGM) for fault prediction based on the data, utilizing the Grey System Theory. The proposed model covers multiple characteristic variables causing communication system faults, monitors and predicts the characteristic variables in the operation of the communication system based on time series, and takes the predicted values as the basis for fault prediction. A certain type of communication system RF module is taken as case study to analyze the performance of the proposed fault prediction model. Through the analysis of the failure mechanism and the actual failure cases of the RF module of the communication system, four characteristic parameters, frequency error, medium power, squelch sensitivity and maximum audio level, are selected The comparative analysis shows that the proposed MGM method has better prediction accuracy than the traditional mean GM (1, 1) model. Besides, the model can process and calculate data with the help of computer software, thus improving the application efficiency of multivariate MGM (1, m) prediction model. The method proposed in this paper provides a feasible solution for predicting the critical faults of complex communication systems.

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A Review of Failure Prediction in Distributed Data Centers

Xie

Zhang

2022

Communications in Computer and Information Science

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Demonstration of a Novel Framework for Proactive Maintenance Using Failure Prediction and Bit Lossless Protection With Autonomous Network Diagnosis System

Cited by 14 publications

References 11 publications

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

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

Study on communication system fault prediction based on multivariate grey model

A Review of Failure Prediction in Distributed Data Centers

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