# Optical Cable Lifespan Prediction Method Based on Autoformer
## 6 Conclusions  
Based on the results of our numerical experiments, this paper introduces a novel approach for predicting the remaining lifespan of optical cables utilizing the Autoformer model. The proposed model leverages deep learning and computational methodologies, utilizing temperature fluctuations, wind loads, and the cable's residual length to predict the cable's lifespan. Experimental results demonstrate that the Autoformer model outperforms other deep learning models, indicating its potential for accurate and reliable lifespan prediction. This research contributes a more effective method to the field of optical cable lifespan prediction, providing network operators and communication services with more accurate residual life predictions. This facilitates timely maintenance scheduling, reduces potential network failure risks, and ensures service continuity and stability. Furthermore, this work also offers enterprises more precise predictions of a cable's remaining life, aiding in planning equipment maintenance and production scheduling, thereby enhancing production efficiency and equipment utilization. These contributions are significant not only in the academic sphere but also have a profound impact on industrial practices and commercial applications.  
Conceptualization, M.N. and Y.L.; methodology, M.N. and Y.L.; software, M.N.; validation, M.N. and J.Z.; formal analysis, M.N.; investigation, M.N.; resources, Y.L.; data curation, M.N. and J.Z.; writing--original draft preparation, M.N.; writing--review and editing, M.N. and Y.L.; visualization, M.N.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Impact of time window length on Bi-LSTM + Attention model performance.  
**Funding:** This research was funded by Wuhan National Laboratory for Optoelectronics and China Southern Power Grid Company Co., Ltd.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to the sensitive nature of the data and legal restrictions preventing public disclosure.  
**Acknowledgments:** The authors are grateful to those who provided support for this paper. [PERSON], representing China Southern Power Grid Company Co., Ltd., provided the data for this paper. [PERSON], representing Wuhan WUTOS Co., Ltd., contributed to the selection of detection parameters for OPGW optical cables, the provision of practical engineering parameters, and suggestions for model optimization. [PERSON], from Central China Normal University, made significant contributions to this work through the computation of new neural network models and other related tasks.  
**Conflicts of Interest:** The authors declare that this study received funding from Wuhan National Laboratory for Optoelectronics and China Southern Power Grid Company Co., Ltd. The funder was involved in the data collection of this study. However, the funder was not involved in the study design, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.