A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication

TL;DR

This paper presents a machine learning framework for real-time predictive maintenance of semiconductor lasers, improving reliability through degradation prediction, anomaly detection, and remaining useful life estimation validated with experimental aging data.

Contribution

The paper introduces a novel ML-based predictive maintenance framework utilizing attention-based GRU and autoencoder models for semiconductor lasers, enhancing accuracy and reliability over existing methods.

Findings

Achieved RMSE of 0.01 in degradation prediction

Detected anomalies with 94.24% accuracy

Provided superior RUL estimation compared to prior models

Abstract

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of semiconductor laser and thus enhancing its reliability. The proposed approach is composed of three stages: i) real-time performance degradation prediction, ii) degradation detection, and iii) remaining useful life (RUL) prediction. First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation. Then, a convolutional autoencoder is used to detect the degradation or abnormal behavior of a laser, given the predicted degradation performance values. Once an abnormal state is detected, a RUL prediction model based on attention-based deep learning is utilized. Afterwards, the estimated RUL is input for decision making and maintenance planning. The proposed framework is validated using experimental data derived from accelerated aging tests conducted for semiconductor tunable lasers. The proposed approach achieves a very good degradation performance prediction capability with a small root mean square error (RMSE) of 0.01, a good anomaly detection accuracy of 94.24% and a better RUL estimation capability compared to the existing ML-based laser RUL prediction models.