Jun Zhang

Bio: Jun Zhang is an academic researcher from Jiangxi University of Science and Technology. The author has contributed to research in topics: Markov decision process & Fault tree analysis. The author has an hindex of 2, co-authored 2 publications receiving 7 citations.

Reliability calculation method based on the Copula function for mechanical systems with dependent failure

Abstract: In order to accurately calculate the reliability of mechanical components and systems with multiple correlated failure modes and to reduce the computational complexity of these calculations, the Copula function is used to represent related structures among failure modes. Based on a correlation analysis of the failure modes of parts of a system, a life distribution model of components is constructed using the Copula function. The type of Copula model was initially selected using a binary frequency histogram of the life empirical distribution between the two components. The unknown parameters in the Copula model were estimated using the maximum likelihood estimation method and the most suitable Copula model was determined by calculating the square Euclidean distance. The reliability of series, parallel, and series–parallel systems was analyzed based on the Copula function, where life was used as a variable to measure the correlation between components. Thus, a reliability model of a system with life correlations was established. Reliability calculation of a particular diesel crank and connecting rod mechanism was taken as a practical example to illustrate the feasibility of the proposed method.

Fault tree analysis method based on probabilistic model checking and discrete time Markov Chain

Abstract: In order to fully consider the dynamic characteristics of event failure in fault tree analysis, the probabilistic model checking and discrete-time Markov chain are applied to the fault tree...

Effect of High-Temperature-Assisted Ultrasonic Deep Rolling on Microstructure and Tribological Properties of Ni-WC Coatings

Abstract: Cermet coatings are post-treated by a new surface microcrystallization technology, namely high-temperature-assisted ultrasonic deep rolling (HT + UDR). The process parameters of ultrasonic deep rolling significantly affect the microstructure and tribological properties of the Ni-WC coatings. In this paper, the samples were treated with different preloading depths (0.20 mm, 0.25 mm, and 0.30 mm), and the microstructure and properties of the coatings were characterized by SEM, EDS, X-ray stress analysis, and micro-Vickers hardness testing. An MMW-1A-type friction and wear tester was used for the dry friction and wear test at room temperature, respectively. Compared with the untreated sample, plastic rheology occurred on the surface of the coatings after HT + UDR, showing a phenomenon of “cutting peaks and filling valleys”. In the treated coatings, visible cracks were eliminated, and the inside of the coating was denser. The surface hard phase was increased as a “skeleton” and embedded with the soft phase, which played a role in strong and tough bonding. After HT + UDR + 0.25 mm treatment, the surface roughness increased by 68%, the microhardness of the surface layer reached a maximum of 726.3 HV0.1, and the residual tensile stress changed from 165.5 MPa to −337.9 MPa, which inhibited the germination and propagation of cracks. HT + UDR improved the wear resistance of the coating in many aspects. The coating after the 0.25 mm preloading depth treatment possessed the smallest friction coefficient and the lowest wear amount, which is 0.04 and 4.5 mg, respectively. The wear form was abrasive wear, and the comprehensive tribological performance is the best.

Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies

Abstract: The stochastic nature of microbial corrosion creates spatial interdependencies among random corrosion parameters and their failure modes. These interdependencies need to be captured for robust offshore system reliability prediction considering complex multispecies biofilms. This research paper presents a hybrid methodology for the prediction of system reliability, considering multiple failure modes’ interdependencies. The methodology integrates the Bayesian Network with Copula-based Monte Carlo (BN-CMC) simulation. The BN captures the dynamic interactions among physio-chemical parameters and microbes to predict the corrosion rate of an offshore system. The random corrosion parameters dependencies and the failure modes that define the performance functions under microbial corrosion are modeled using CMC. The methodology is assessed with an example, and the impact of dynamic interactions of the parameters and their failure modes on the system reliability is investigated. The results reveal that the system's probability of failure differs diversely as the degree of dependencies among the random corrosion parameters and their failure modes increases. The proposed methodology can predict the failure indexes that could aid system integrity management for a sustainable offshore operation experiencing microbial corrosion.

A new Markov-based model for reliability optimization problems with mixed redundancy strategy

Abstract: This paper revisits the redundancy allocation problem (RAP) from the viewpoint of the mixed redundancy strategy. This powerful strategy has some major weaknesses originated from its complicated formulation. Previously, for estimating the reliability of this strategy a lower bound formulation was introduced. In an attempt to improve the complicated, time-consuming, and imprecise lower bound formulation previously proposed, in this paper, an exact Markov based approach is developed. Being a powerful and robust tool, it is especially advantageous for its short computation time. This is demonstrated by its implementation to solve a well-known benchmark test problem. Based on the results, the new approach finds better solutions with higher reliability values than before, with a significant reduction in the computation time.

Forecast Analysis of Research Chance on AES Algorithm to Encrypt during Data Transmission on Cloud Computing

Abstract: Cloud computing can be described as a network technology among users to share data, resources and even services. Because million users have the same rights to use network for data transmission, data is vulnerable to be hacked by irresponsible person. The concentration of security on current system can only be found on data storage on cloud, meanwhile less concentration is found on data transmission. As we determine security can be potential issue, the proposed system (encryption) will secure data transfer. The proposed system, Advanced Encryption Standard (AES) algorithm, will anticipate every irresponsible person to hack cloud system. The proposed algorithm will encrypt and decrypt information on data transmission and storage. To improve published research on security of data transmission by using AES algorithm is the paper purpose. In order to do that, the research will use several mathematical methods. There are Markov Chain and Forecasting methods. Research results show that there are possibilities to research on AES algorithm for security of data transmission in the near future by 29% in 2023.

Nonparametric-copula-entropy and network deconvolution method for causal discovery in complex manufacturing systems

Abstract: To clarify the causality among process parameters is a core issue of data-driven production performance analysis and product quality optimization. The difficulty lies in accurately measuring and distinguishing direct and indirect associations of complex manufacturing systems. In this work, the nonparametric-copula-entropy and network deconvolution method is proposed for causal discovery in complex manufacturing systems. Firstly, based on copula theory and kernel density estimation method, the nonparametric-copula-entropy is introduced to improve the accuracy of association measurement between parameters, and its superiority is verified by comparing with the results of different association measurement methods. Then, the global association matrix is constructed by the nonparametric-copula-entropy, and network deconvolution method is employed to extract the direct information from the global association matrix. The proposed method is tested by using an open gene expression dataset. Finally, as an experimental application, the causal analysis for a diesel engine production line is carried out by the proposed method. The results show that the proposed method can reveal causal relationship between process parameters and quality parameters in the diesel engine production line well, which provide theoretical guidance and implementation approach for the optimal control of complex manufacturing system.

A comparison of estimation methods for reliability function of inverse generalized Weibull distribution under new loss function

Abstract: In this paper, we focussed on the scale parameter and reliability estimations of the inverse generalized Weibull distribution. Both classical and Bayesian approaches are considered with various los...