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An efficient reliability analysis strategy for low failure probability problems
Runan Cao,Zhili Sun,Jian Wang,Fanyi Guo 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.78 No.2
For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.
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Runan Guo,Ziye Zhang,Chong Lin,Yuming Chu,Yongmin Li 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.3
This paper considers the finite time state estimation problem of complex-valued bidirectional associativememory (BAM) neutral-type neural networks with time-varying delays. By resorting to the Lyapunov functionapproach, the Wirtinger inequality and the reciprocally convex approach, a delay-dependent criterion in terms ofLMIs is established to guarantee the finite-time boundedness of the error-state system for the addressed system. Meanwhile, an effective state estimator is designed to estimate the network states through the available outputmeasurements. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed results.
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Kinetics of Horseradish Peroxidase-Catalyzed Nitration of Phenol in a Biphasic System
( Mingming Kong ),( Yang Zhang ),( Qida Li Runan Dong ),( Haijun Gao ) 한국미생물 · 생명공학회 2017 Journal of microbiology and biotechnology Vol.27 No.2
The use of peroxidase in the nitration of phenols is gaining interest as compared with traditional chemical reactions. We investigated the kinetic characteristics of phenol nitration catalyzed by horseradish peroxidase (HRP) in an aqueous-organic biphasic system using n-butanol as the organic solvent and NO<sub>2</sub> - and H<sub>2</sub>O<sub>2</sub> as substrates. The reaction rate was mainly controlled by the reaction kinetics in the aqueous phase when appropriate agitation was used to enhance mass transfer in the biphasic system. The initial velocity of the reaction increased with increasing HRP concentration. Additionally, an increase in the substrate concentrations of phenol (0-2 mM in organic phase) or H<sub>2</sub>O<sub>2</sub> (0-0.1 mM in aqueous phase) enhanced the nitration efficiency catalyzed by HRP. In contrast, high concentrations of organic solvent decreased the kinetic parameter V<sub>max</sub>/K<sub>m</sub>. No inhibition of enzyme activity was observed when the concentrations of phenol and H<sub>2</sub>O<sub>2</sub> were at or below 10 mM and 0.1 mM, respectively. On the basis of the peroxidase catalytic mechanism, a double-substrate ping-pong kinetic model was established. The kinetic parameters were K<sub>m</sub> <sup>H2O2</sup> = 1.09 mM, K<sub>m</sub> <sup>PhOH</sup> = 9.45 mM, and V<sub>max</sub> = 0.196 mM/min. The proposed model was well fit to the data obtained from additional independent experiments under the suggested optimal synthesis conditions. The kinetic model developed in this paper lays a foundation for further comprehensive study of enzymatic nitration kinetics.
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Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM
Dong Liang,Chen Zeyu,Hua Runan,Hu Siyuan,Fan Chuanhan,Xiao xingxin 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.3
Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and nonstationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.
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