【作者】 武茂浦； 刘永阔； 于巍峰； 彭敏俊； 周文； 刘鹏飞；

【Author】 WU Mao-pu;LIU Yong-kuo;YU Wei-feng;PENG Min-jun;ZHOU Wen;LIU Peng-fei;Harbin Engineering University;

【机构】 哈尔滨工程大学；

【摘要】 保证核动力装置的安全运行一直是核能发展的一个重要研究课题。因此,国内外提出了各种方法用于核动力装置的故障诊断,以辅助操作员的工作。BP神经网络具有很强的非线性映射能力,能够对复杂信息进行快速处理、识别和分类,因此可以用来对系统设备的状态变化进行识别和判断。而符号有向图(Signed Directed Graph,SDG)可以形象明确地表示过程变量之间的关系和故障传播的路径,且具有建模方便、推理灵活等特点。利用SDG方法对诊断结果进行推理验证的同时,还可以给出故障的播路径,有利于操作员快速采取有效的措施,确保核电站的安全、稳定运行。本文首先利用BP神经网络对系统的运行状态进行诊断识别,然后使用SDG方法对诊断结果进行推理验证,确保诊断的正确性和可靠性。最后,通过仿真数据对该方法进行验证。更多还原

【Abstract】 It is a significant issue for nuclear power development to keep the nuclear power plant operating safely;therefore,a variety of methods has been proposed to fault diagnosis,in order to assist the work of the operators.Back Propagation(BP) neural network has a strong nonlinear mapping capacity and it can process,identify and class the complex information rapidly,so it can be used to identify and judge the state of the system equipment.The complex relationship between the parameters and the fault propagation paths can be showed clearly by signed directed graph(SDG),and it has advantages of establishing model conveniently,inference flexible and so on.SDG is used to verify the diagnosis results and achieve the fault propagation path,which is advantageous for the operators to take effective measures to ensure the safe operation of nuclear power plant.BP neural network was used to diagnose the operating state recognition and the SDG method was used to verify the diagnosis results in this paper.Finally,the proposed method is to be verified by simulator.更多还原