Reliability Estimation Using Markov Chain Monte Carlo–Based Tail Modeling

Bayrak, Gamze; Acar, Erdem

doi:10.2514/1.j055947

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…It is very useful when the maximum and minimum values of the original data are unknown or the outliers control the Max-Min standardization. Z-score standardization is currently the most widely used standardization method [21].…”

Section: Data Processingmentioning

confidence: 99%

“…Markov process is a random process with no after effect. e so-called no after effect refers to that when the state of a random process at time t 0 is known, the state of a random process at time t(t > t 0 ) is only related to the state of time t 0 , but not to the state of a process before time t 0 [20,21]. ose Markov processes with discrete time and state are called Markov chains, as shown in Figure 6.…”

Section: Cumulative Anomaly Detection Based On Dtmcmentioning

confidence: 99%

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Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Yang

Najafi

2022

Security and Communication Networks

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There exists an enormous volume of data in the database system, which is accountable for the storage of data and organization of data. The intruders can breach the security system of database and steal the important information. Therefore, it is of great significance to carry out the cumulative anomaly identification of the security database. In view of the shortcomings of traditional anomaly detection methods in detection performance and poor effect of anomaly recognition, this paper proposes a cumulative anomaly recognition method based on discrete Markov chain for security database. First, the sniffer is used to read the user access behaviour data, and then, it is processed, that is, standardized processing. Then, the segmentation method is used to extract the user behaviour features, and the normal feature data and abnormal feature data are obtained. Finally, the state sequence generated by the discrete Markov chain is used to calculate the state probability, which is used to evaluate the abnormal process behaviour. The proposed method in this paper is based on the Markov chain and can be used for better anomaly recognition. The results are obtained in terms of sensitivity score, precision score, and F1-score. The results are also compared with the results obtained by using some of the state-of-the-art traditional techniques. The comparison clearly indicated that the proposed method is more effective as compared to the tradition methods. The proposed method has the highest F1-score of 0.8586, and then the traditional methods have F1-scores of 0.7233, 0.8236, and 0.7562 for methods 1, 2, and 3, respectively.

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Section: Data Processingmentioning

confidence: 99%

Section: Cumulative Anomaly Detection Based On Dtmcmentioning

confidence: 99%

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Yang

Najafi

2022

Security and Communication Networks

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“…In practical engineering structures, especially complex structures such as large-span asymmetric suspended structure, the dynamic equations M, C, and K represented by Equation ( 1) are not easily obtained. Based on the theory of dynamic analysis in FEM, the parameters ω in Equation (9) and ϕ in Equation ( 8) can be easily obtained after precise FEA modeling of structures. Essentially, Equation (1) between Equations ( 8) and ( 9) is completely equivalent in mechanical terms, so the above method has broad engineering applications.…”

Section: Wind-induced Vibration Equation In Generalized Coordinatesmentioning

confidence: 99%

“…In general, the structural response subjected to the random excitation could be analyzed by three methods: Monte Carlo method, 9,10 time‐domain approach, and frequency‐domain approach 11–13 . In commonly used Monte Carlo method, time‐history curves are derived from the power spectral density function of the random wind excitation, and the time‐history curves of the structural response and the variance of response variables could be obtained based on the dynamic and statistical analysis 14,15 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic response of asymmetric suspended structure subjected to random wind excitation

Tian,

Yao,

Pan

et al. 2024

Structural Design Tall Build

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SummarySuspended structures, regarded as the expression of structural beauty, have attracted the focus of many architects and engineers. With the complexity of dynamic responses of the asymmetric suspended structures subjected to the random alongside wind excitation, an innovative method was proposed through combining the finite element method (FEM), the complex mode method (CMM), the pseudo excitation method (PEM), and the quadratic decomposition method (QDM) of frequency response function. The method could obtain unified closed‐form solutions of spectral moment and variance of node displacement and velocity and acceleration of asymmetric structures and its correctness was verified by existing numerical approaches. The proposed method is applicable to the random wind‐induced vibration response analysis of diverse linear complex structures. Notably, its efficacy lies in its circumvention of numerical integration, endowing it with high computational efficiency and accuracy. Extended comparative studies and discussion were also performed on effect of suspended span and comparisons of normal framed structure and symmetry structure. The results showed that the suspended span presented positive relation to horizontal displacement and inverse tendency to horizontal acceleration of the whole structure and vertical and horizontal acceleration of the columns near to suspended part should also be considered in engineering design.

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“…Building a system reliability model is essential in reliability design [7,8]. Currently, fault tree analysis (FTA) [9,10], reliability block diagram (RBD) [11,12], failure mode and effect analysis (FMEA) [13,14], event tree (ET) [15], the minimum cut set method [16,17], dynamic fault tree (DFT) [18,19], and Markov chain (MC) [20,21] have mature theoretical foundations. These are the primary technical means of aircraft system reliability evaluation both domestically and internationally.…”

Section: Introductionmentioning

confidence: 99%

A Three-State Space Modeling Method for Aircraft System Reliability Design

Wang,

Feng

et al. 2023

Machines

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Reliability is an inherent attribute of a system through optimal system design. However, during the aircraft system development process, the reliability evaluation and system function design efforts are often disconnected, leading to a divide between reliability experts and system designers in their work schedule. This disconnect results in an inefficient aircraft system reliability optimization process, known as the “two-skin” phenomenon. To address this issue, a three-state space model is proposed. Firstly, an analysis was conducted on the relationship between the system function architecture developed by the system designers and the reliability evaluation performed by the reliability experts. Secondly, based on the principle of function flow, the state of failure was categorized into “physical failure” and “non-physical failure”. Additionally, a new state of “function loss” was introduced as the third state for the system, in addition to the traditional states of “normal” and “faulty”. Thirdly, through the state of “Function loss”, an effective integration of system fault modes and function modes was achieved, leading to an optimized system reliability model. A three-state space modeling method was then developed by transforming the system function architecture into a system reliability model. Finally, this new model was applied to an aircraft’s rudder and fly-by-wire control system. The results demonstrate that the function architecture at the design stage of the system can be accurately transformed into the new three-state space model. The structure aligns closely with the function architecture and can be effectively utilized in quantitative system reliability calculations. In this way, the process of ensuring system reliability can be seamlessly integrated into the system optimization design process. This integration alleviates the issue of disjointed work between reliability experts and system designers, leading to a more streamlined and efficient aircraft system optimization process.

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Reliability Estimation Using Markov Chain Monte Carlo–Based Tail Modeling

Cited by 8 publications

References 21 publications

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

Dynamic response of asymmetric suspended structure subjected to random wind excitation

A Three-State Space Modeling Method for Aircraft System Reliability Design

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