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Volume 39 Issue 6
Dec.  2018
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Shen Jiangfei, Pan Tiancheng, Mao Xiaoming, Wu Tianhao, Gu Fang. Study on Condition Detection of Major Equipment in Nuclear Power Plants Based on Fuzzy Synthetic Assessment[J]. Nuclear Power Engineering, 2018, 39(6): 104-110. doi: 10.13832/j.jnpe.2018.06.0104
Citation: Shen Jiangfei, Pan Tiancheng, Mao Xiaoming, Wu Tianhao, Gu Fang. Study on Condition Detection of Major Equipment in Nuclear Power Plants Based on Fuzzy Synthetic Assessment[J]. Nuclear Power Engineering, 2018, 39(6): 104-110. doi: 10.13832/j.jnpe.2018.06.0104

Study on Condition Detection of Major Equipment in Nuclear Power Plants Based on Fuzzy Synthetic Assessment

doi: 10.13832/j.jnpe.2018.06.0104
  • Publish Date: 2018-12-15
  • Focusing on the major equipment management detailed rules, equipment operation characteristics and requirements of nuclear power plants, a comprehensive evaluation method for major equipment health status of nuclear power plants is proposed. According to the characteristics of equipment monitoring of nuclear power plants, a multi-level indicator system model based on monitoring tasks is established. Based on the potential failure modes of equipment components, the fault phenomena and the equipment monitoring tasks are analyzed, and the monitoring tasks membership function models are constructed. Summarizing the practical assessment experience of the experts, this paper presents that indexes weight are shared by the multiple monitoring tasks, the weights are inherited by the most severely degraded monitoring task, and the indicator status of the equipment is obtained. Through the improved Analytic Hierarchy Process, the initial weights of each indicator are assigned, and the hierarchical variable weight theory models based on the indicator status level are proposed with a balanced consideration of the key indicators deterioration. The established evaluation method is applied to the health status assessment of the nuclear island main pump shaft seal system. The results show that the method is reliable and practical, and can effectively characterize the actual operational health status of major equipment.

     

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