Uncertainty Expression and Propagation in the Risk Assessment of Uncertain Random System

Hu, Lunhu; Kang, Rui; Pan, Xing; Zuo, Dujun

doi:10.1109/jsyst.2020.2990679

Cited by 18 publications

(3 citation statements)

References 44 publications

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“…Belief reliability centered maintenance and supportability optimization (i) Maintenance optimization model: maintenance indexes and analysis for repairable systems [153], optimization of the level of repair considering spare stocks [154] (ii) Spare parts optimization: variety optimization [155,156], quantity optimization [45,157], depot location optimization [158][159][160]…”

Section: Fieldmentioning

confidence: 99%

Belief Reliability: A Scientific Exploration of Reliability Engineering

Zhang,

Li,

et al. 2024

J. of Syst. Eng. Electron.

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This paper systematically introduces and reviews a scientific exploration of reliability called the belief reliability. Beginning with the origin of reliability engineering, the problems of present theories for reliability engineering are summarized as a query, a dilemma, and a puzzle. Then, through philosophical reflection, we introduce the theoretical solutions given by belief reliability theory, including scientific principles, basic equations, reliability science experiments, and mathematical measures. The basic methods and technologies of belief reliability, namely, belief reliability analysis, function-oriented belief reliability design, belief reliability evaluation, and several newly developed methods and technologies are sequentially elaborated and overviewed. Based on the above investigations, we summarize the significance of belief reliability theory and make some prospects about future research, aiming to promote the development of reliability science and engineering.

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Section: Fieldmentioning

confidence: 99%

Belief Reliability: A Scientific Exploration of Reliability Engineering

Zhang,

Li,

et al. 2024

J. of Syst. Eng. Electron.

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“…Meanwhile, uncertainty theory [17] was used to express subjects' belief degrees in the experiment, and correspondingly, the difficulty of choice was evaluated by the entropy [18] defined in the uncertainty theory. The reasons are clear: It has been demonstrated in several studies that, compared with other theories such as subjective probability [19], evidence theory [20], and possibility theory [21], uncertainty theory is more suitable for expressing human belief degree because it leads to a more robust, rigorous, and clear result that can significantly facilitate decision-making processes [22][23][24][25]. In addition, replacing the original guessing question of uncertain binary choice as its complementary question does not essentially change the difficulty of choice, which can be well reflected by the entropy in uncertainty theory that satisfies the property of symmetry.…”

Section: Introductionmentioning

confidence: 99%

Human Decision Time in Uncertain Binary Choice

Pan

Ding

et al. 2022

Symmetry

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Decision time, also known as choice reaction time, has been frequently discussed in the field of psychology. The Hick–Hyman Law (HHL) has been a fundamental model that has revealed the quantitative relationship between the mean choice reaction time of human and the information entropy of stimuli. However, the HHL is only focused on rule-based behavior in which rules for selecting response according to stimulus are certain and neglects to model the knowledge-based behavior in which choices are uncertain and influenced by human belief. In this article, we explored the decision time related to one basic knowledge-based behavior—uncertain binary choice, where selections of response are determined by human belief degrees but not by stimuli uncertainties. Two experiments were conducted: one for verifying the HHL and the other for uncertain binary choice. The former (experiment) demonstrated the effectiveness of the HHL, and the latter one indicated that there is an exponential relationship existing between decision time and entropy of belief degree in uncertain binary choice. Moreover, data obtained from both experiments showed that the disturbance term of decision time should not be seen as probabilistic as existing studies have assumed, which highlighted the necessity and advantage of uncertain regression analysis.

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“…Among them, applying the first hitting time into an accelerated degradation model, Li et al 15 gave the reliability and lifetime evaluations; the simulation case and sensitivities were provided for showing the accuracy and efficiency of risk control by the proposed method. A new uncertainty expression framework is proposed by Hu et al 16 to correctly represent the different sources of uncertainty that exist in the system. They also pioneered the introduction of level 2 distributions into the operating criteria of Boolean indeterminate stochastic systems to better satisfy the demands of the actual risk assessment 17 .…”

Section: Introductionmentioning

confidence: 99%

Optimal control problem of the uncertain second‐order circuit based on first hitting criteria

Jin

Xia

Chen

2020

Math Methods in App Sciences

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First hitting criteria of a system are to initially achieve some performance indeces of the target state set. This paper primarily investigates the optimal control problem of the uncertain second-order circuit based on first hitting criteria. First, considering time efficiency and different from the ordinary expected utility criterion over an infinite time horizon, two first hitting criteria which are reliability index and reliable time criteria are innovatively proposed. Second, assuming the circuit output voltage as an uncertain variable when the historical data is lacking, we better model the real circuit system with the uncertain second-order differential equation which is essentially the uncertain fractional-order differential equation. Then, based on the first hitting time theorem of the uncertain fractional-order differential equation, the distribution function of the first hitting time under the second-order circuit system is proposed and the uncertain second-order circuit optimal control model (reliability index and reliable time-based model) is transformed into corresponding crisp optimal problem. Lastly, analytic expressions of the optimal control for the reliability index model are obtained. Meanwhile, sufficient condition and guidance for parameters for the optimal solution of the reliable time-based model are derived, and corresponding numerical examples are also given to demonstrate the fluctuation of our optimal solution for different parameters.

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Uncertainty Expression and Propagation in the Risk Assessment of Uncertain Random System

Cited by 18 publications

References 44 publications

Belief Reliability: A Scientific Exploration of Reliability Engineering

Belief Reliability: A Scientific Exploration of Reliability Engineering

Human Decision Time in Uncertain Binary Choice

Optimal control problem of the uncertain second‐order circuit based on first hitting criteria

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