Abstract:
Background Fiber laser coherent beam combining technology enables high-power laser output through precise phase control of multiple laser channels. However, factors such as phase control accuracy, optical intensity stability, communication link reliability, and environmental interference can degrade system performance.
Purpose This study aims to address the challenge of anomaly detection in phase control for large-scale fiber laser coherent combining by proposing a novel deep learning-based detection method.
Methods First, ten-channel fiber laser coherent combining data were collected, system control processes and beam combining principles were analyzed, and potential anomalies were categorized to generate a simulated dataset. Subsequently, an EMA-Transformer network model incorporating a lightweight Efficient Multi-head Attention (EMA) mechanism was designed. Comparative experiments were conducted to evaluate the model's performance. Finally, an eight-beam fiber laser coherent combining experimental setup was established, and the algorithm was deployed using TensorRT for real-time testing.
Results The proposed algorithm demonstrated significant improvements, achieving approximately 50% higher accuracy on the validation set and a 2.20% enhancement on the test set compared to ResNet50. In practical testing, the algorithm achieved an inference time of 2.153 ms, meeting real-time requirements for phase control anomaly detection.
Conclusions The EMA-Transformer model effectively addresses anomaly detection in fiber laser coherent combining systems, offering superior accuracy and real-time performance. This method provides a promising solution for enhancing the stability and reliability of high-power laser systems.
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