一种联邦学习的光模块群体寿命和早期失效预警方法
By using a federated learning framework and a TA-LSTM model, the problems of capturing the progressive degradation trajectory of optical modules and mining common patterns among groups are solved, enabling accurate prediction and early warning of optical module lifespan, and reducing the false alarm and missed detection rates.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHENGDU GUANGCHUANGLIAN CO LTD
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to capture the gradual degradation trajectory of optical module performance parameters, making it difficult to identify latent and gradual failures in their early stages. There is a lack of exploration into common degradation patterns among different individual groups, and data from each workstation is isolated, lacking collaborative analysis and early warning mechanisms, resulting in low fault location efficiency and a high rate of missed detections.
Employing a federated learning framework, the system collects optical power and bias current through a degradation trajectory feature extraction module to construct degradation feature vectors. The feature vectors are then processed using a TA-LSTM model. Client and cloud models are deployed for gradient aggregation and early warning, establishing a dynamic fluctuation benchmark and enabling cross-client collaborative analysis and early warning.
Without exposing the raw data, it accurately captures the gradual degradation trajectory and abrupt failure precursors of optical module performance parameters, significantly improving the accuracy and real-time performance of individual optical module lifetime prediction, reducing the false alarm rate and missed detection rate, and realizing collaborative early warning across clients.
Smart Images
Figure CN122137463B_ABST