Liu Zhilong, Liu Jie, Huang Yanping, Li Tongxi, Nie Changhua, Xia Yanjun, Zhan Li, Tang Zhangchun, Zhang Lin
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Institute of Reactor Power Engineering, Nuclear Power Institute of China, Chengdu 610000, China.
Materials (Basel). 2022 Jan 19;15(3):757. doi: 10.3390/ma15030757.
The number of fault samples for the new nuclear valve is commonly rare; thus, the machine learning algorithm is not suitable for the fault prediction of this kind of equipment. In order to overcome this difficulty, this paper proposes a novel method for the fault critical point prediction of the gate valve based on the characteristic analysis of the operation process variables. The operation process of gate valve switch often contains various fault characteristics and information, and this method first adopts the Shannon entropy to describe the power spectrum of vibration signal relevant to the operation process of gate valve switch, and then employs the mean value of the power spectrum entropy as an indirect process variable and further investigates the differences between the indirect process variable under the healthy state and the fault state with a different fault degree. In addition, the power signal of the gate valve is also employed as the direct process variable and the features of the direct process variable under the healthy state and the fault state with different fault degrees are also investigated. Based on the previous indirect process variable and direct process variable, the prediction approach for the critical point of the gate valve fault is established by analyzing the change in the indirect process variable and direct process variable before and after faults. Finally, the data of a nuclear gate valve experiment are employed to demonstrate the feasibility of the proposed method and the results show that the proposed method can effectively predict the fault critical point of the mentioned nuclear gate valve. If the diagnostic threshold is set properly, the critical point prediction of a nuclear gate valve fault can be realized as 100% or close to 100%. Furthermore, the proposed method can be directly applied to the nuclear gate valve in a nuclear power plant to improve the operation reliability of the valve. At the same time, the method can be applied to the fault diagnosis and prediction of valves in other fields, such as the chemical industry.
新型核阀门的故障样本数量通常很少；因此，机器学习算法不适用于这类设备的故障预测。为克服这一困难，本文提出了一种基于阀门操作过程变量特征分析的闸阀故障临界点预测新方法。闸阀开关的操作过程通常包含各种故障特征和信息，该方法首先采用香农熵来描述与闸阀开关操作过程相关的振动信号的功率谱，然后将功率谱熵的平均值作为间接过程变量，并进一步研究健康状态下和不同故障程度的故障状态下间接过程变量之间的差异。此外，闸阀的功率信号也被用作直接过程变量，并研究健康状态下和不同故障程度的故障状态下直接过程变量的特征。基于先前的间接过程变量和直接过程变量，通过分析故障前后间接过程变量和直接过程变量的变化，建立了闸阀故障临界点的预测方法。最后，采用核闸阀实验数据验证了所提方法的可行性，结果表明所提方法能够有效地预测所述核闸阀的故障临界点。如果诊断阈值设置得当，核闸阀故障的临界点预测准确率可达100%或接近100%。此外，所提方法可直接应用于核电站中的核闸阀，以提高阀门的运行可靠性。同时，该方法可应用于其他领域（如化工行业）阀门的故障诊断与预测。
