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

• URL: http://arxiv.org/abs/2211.02842v1
• Date: Sat, 5 Nov 2022 07:53:02 GMT
• Title: A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
• Authors: Khouloud Abdelli, Helmut Griesser, and Stephan Pachnicke
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate [1.7010199949406575]
  We investigate how Machine Learning (ML) techniques can aid in reducing the computational effort and implicitly the time required to estimate the voltage drop in Integrated Circuits (ICs) Our approach leverages ASICs' electrical, timing, and physical to train ML models, ensuring adaptability across diverse designs with minimal adjustments. This study illustrates the effectiveness of ML algorithms in precisely estimating IR drop and optimizing ASIC sign-off.
  arXiv  Detail & Related papers  (2025-02-07T21:31:13Z)
• Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration [49.03824084306578]
  We propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application. We pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.
  arXiv  Detail & Related papers  (2024-12-18T10:36:46Z)
• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more-efficient metric for performance estimation. We extend the power law analytical function to predict domain-specific pre-training loss based on FLOPs across data sources. We employ a two-layer neural network to model the non-linear relationship between multiple domain-specific loss and downstream performance.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• Sparse Low-Ranked Self-Attention Transformer for Remaining Useful Lifetime Prediction of Optical Fiber Amplifiers [0.0]
  We propose Sparse Low-ranked self-Attention Transformer (SLAT) as a novel Remaining useful lifetime (RUL) prediction method. SLAT is based on an encoder-decoder architecture, wherein two parallel working encoders extract features for sensors and time steps. The implementation of sparsity in the attention matrix and a low-rank parametrization reduce overfitting and increase generalization.
  arXiv  Detail & Related papers  (2024-09-22T09:48:45Z)
• Model-based Optimization of Superconducting Qubit Readout [59.992881941624965]
  We demonstrate model-based readout optimization for superconducting qubits. We observe 1.5% error per qubit with a 500ns end-to-end duration and minimal excess reset error from residual resonator photons. This technique can scale to hundreds of qubits and be used to enhance the performance of error-correcting codes and near-term applications.
  arXiv  Detail & Related papers  (2023-08-03T23:30:56Z)
• Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks [63.614519238823206]
  Spiking Neural Networks (SNNs) are a practical choice when the target hardware reaches the edge of computing. We employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error.
  arXiv  Detail & Related papers  (2022-11-22T15:08:05Z)
• Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder [0.0]
  We propose a new data-driven approach to predict the degradation trend without requiring any specific knowledge or using any physical model. The proposed approach is based on an unsupervised technique, a conditional variational autoencoder, and validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge emitting laser reliability data. The experimental results confirm that our model (i) achieves a good degradation prediction and generalization performance by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based anomaly detection techniques, and (iii) helps to shorten the aging tests by early predicting the failed devices
  arXiv  Detail & Related papers  (2022-11-05T08:10:11Z)
• Machine Learning based Laser Failure Mode Detection [0.0]
  We propose a data-driven fault detection approach based on Long Short-Term Memory (LSTM) recurrent neural networks to detect the different laser degradation modes. attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy.
  arXiv  Detail & Related papers  (2022-03-19T09:46:19Z)
• Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory [0.0]
  We propose a novel learning model based on long short-term memory (LSTM) to detect, locate, and estimate the reflectance of fiber reflective faults. The experimental results prove that the proposed method achieves a good detection capability and high localization accuracy within short measurement time.
  arXiv  Detail & Related papers  (2022-03-19T08:45:45Z)
• Uncertainty-aware Remaining Useful Life predictor [57.74855412811814]
  Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate. In this work, we consider Deep Gaussian Processes (DGPs) as possible solutions to the aforementioned limitations. The performance of the algorithms is evaluated on the N-CMAPSS dataset from NASA for aircraft engines.
  arXiv  Detail & Related papers  (2021-04-08T08:50:44Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.