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非平稳水流激励下水轮发电机组可靠性综合评估

Reliability comprehensive evaluation of water turbine generator unit under non-stationary water flow excitation
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摘要 针对在非平稳水流激励作用下水轮发电机组同时发生振动失效和疲劳失效的情形,提出了一种非平稳水流激励下水轮发电机组可靠性综合评估方法。首先,通过分析非平稳水流激励下水轮发电机组的振动失效和疲劳失效,建立了水轮发电机组系统贝叶斯网络模型;然后,在分析机组动力学特性的基础上,基于迁移学习和Kriging模型分别对水轮发电机组的振动可靠性和振动疲劳可靠性进行分析;最后,引入共因失效因子,提出了非平稳水流激励下水轮发电机组可靠性综合评估方法,并通过实例验证所提出方法的有效性。实例分析表明,在240 MW下考虑共因失效和不考虑共因失效的可靠度误差为0.051 3,机组受到的非平稳水流激励越大,共因失效对机组的可靠度影响越大。 Here,aiming at simultaneous occurrence of vibration failure and fatigue failure in water turbine generator unit under non-stationary water flow excitation,a reliability comprehensive evaluation method for water turbine generator unit under non-stationary water flow excitation was proposed.Firstly,by analyzing vibration failure and fatigue failure of the unit under non-stationary water flow excitation,Bayesian network model of the unit system was established.Then,based on dynamic characteristics analysis of the unit,vibration reliability and vibration-fatigue reliability of the unit were analyzed using transfer learning and Kriging model,respectively.Finally,by introducing a common cause failure factor,the reliability comprehensive evaluation method for the unit under non-stationary water flow excitation was proposed,and its effectiveness was verified with actual examples.Actual example analyses showed that the reliability error of considering common cause failure and not considering common cause failure under the unit output power of 240 MW is 0.0513;the larger the non-stationary water flow excitation on the unit,the larger the impact of common cause failure on the reliability of the unit.
作者 刘福秀 李兆军 杨统雨 李飞标 丁江 LIU Fuxiu;LI Zhaojun;YANG Tongyu;LI Feibiao;DING Jiang(College of Civil and Architectural Engineering,Guangxi University,Nanning 530004,China;College of Mechanical Engineering,Guangxi University,Nanning 530004,China)
出处 《振动与冲击》 EI CSCD 北大核心 2024年第17期79-90,共12页 Journal of Vibration and Shock
基金 国家自然科学基金项目(51465001,51905113)。
关键词 水轮发电机组 非平稳 可靠性综合评估 贝叶斯网络 KRIGING模型 water turbine generator unit non-stationary reliability comprehensive evaluation Bayesian network Kriging model
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