Performance Prediction Method of Laser Device Energy Component Based on Neural Network

Author:

Yun Tianyou,Wang Xiaoli,Zhou Xiaowei,An Baoran,Liu Jin,Ni Zhigao

Publisher

Springer Nature Switzerland

Reference13 articles.

1. Ren Q, Gao H et al (2020) Transient fault diagnosis algorithm based on feature classification and its application in high-power laser facility. In: Chinese control and decision conference (CCDC), pp 4705–4709

2. Tianyou YUN, Xiaoli WA et al (2020) Fault prediction for energy module in laser equipment based on the analysis of current residual characterstics. In: China Automation Congress (CAC), pp 231–235

3. Tong JL, Chen DH, Qi Z et al (2012) Fault protective measures and EMI depressing approach for power conditioning systems in high power lasers. In: Proceedings of the CSEE, vol 32(28), pp 97–102

4. Hao, S., Guiyou, L., et al.: Modeling and fault analyzing for power conditioning system of solid-stata laser facility. High Power Laser Particle Beanms 1, 1–8 (2019)

5. Jing C, Weiqing W, Shan H (2013) Noise prediction of wind turbines based on regression analysis and BP neural network. Noise Vibr Control 33(6):49–52

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