Prediction of failures in fiber-optic information transmission systems

The article discusses a method for predicting failures in fiber-optic data transmission systems using a self-analysis mechanism. The proposed method is based on the use of machine learning algorithms that can adapt to changing operating conditions by automatically selecting or retraining models. The method includes the stages of data collection and preprocessing, feature extraction, construction of predictive models and their dynamic optimization. The self-analysis mechanism provides continuous assessment of the accuracy of forecasts and allows timely adjustment of model parameters. Testing on actual data showed high forecast accuracy and the superiority of the proposed method over traditional approaches. The results are visualized using error and deviation graphs, confirming the effectiveness of the proposed method.

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