Application of Bayesian Networks in Reliability Evaluation

Cai, Baoping; Liu, Yonghong; Liu, Zengkai; Chang, Yuanjiang; Jiang, Lei

doi:10.1007/978-981-13-6516-4_1

Cited by 28 publications

(34 citation statements)

References 93 publications

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“…We substitute the known parameters of system u s into equation (14) to obtain the value of its FPIR 0 i . We serve all the values of 1 À FPIR 0 i of the six systems in 1 year as the observed values of the root nodes in the improved Fuzzy Dynamic Bayesian Network.…”

Section: Analysis and Verification Of Resultsmentioning

confidence: 99%

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

Zhi-qiang

Zhu

Zhang

et al. 2020

International Journal of Distributed Sensor Networks

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The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

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Section: Analysis and Verification Of Resultsmentioning

confidence: 99%

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

Zhi-qiang

Zhu

Zhang

et al. 2020

International Journal of Distributed Sensor Networks

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“…Nowadays, Bayesian network (BN) [37] or dynamic Bayesian network (DBN) [38] have many applications in fault diagnosis [39] and are increasingly applied in RUL prediction [8]. In the existing literature, Cai et al [8] proposed a hybrid RUL estimation approach of structure systems considering the influence of multiple causes by using DBNs.…”

Section: B Data-driven Approachesmentioning

confidence: 99%

Remaining Useful Performance Estimation for Complex Analog Circuit Based on Maximal Information Coefficient and Bidirectional Gate Recurrent Unit

et al. 2020

IEEE Access

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Degradation of circuit components are typically accompanied by a deviation in component parameters from their normal values, which can ultimately influence the stable operation of complex analog circuit. To address this concern, remaining useful performance (RUP), regarded as the useful performance from the current time to the end of performance, is an effective way to ensure system safety by providing early warning of failure and enabling forecast maintenance. In this paper, a novel RUP estimation method based on the two-stage maximal information coefficient (TSMIC) and bidirectional gate recurrent unit (Bi-GRU) network is proposed. Initially, the run to failure data of the circuit in real-time is obtained by RT-LAB hardware-in-the-loop. Additionally, to obtain suitable features reflecting degradation trend over cycles, a TSMIC method is proposed to eliminate features hardly changing with degradation cycle in the first stage, mine mutual information between features in the second stage. Furthermore, the linear regression model is used as a performance evaluation to retain the original pattern in the selected features. Through the fusion of the selected multi-features, health indicators of different circuit components are constructed. Ultimately, the deep Bi-GRU unit network, which can extract representative time-series information and explore subtle differences of the degradation cycles, is used to generate prediction results. The proposed framework is verified through a case study on the complex analog circuit, and comparisons with other state-of-the-art methods are presented. The experimental results of the case study show the effectiveness and superiority of the proposed approach.

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“…Information fusion is still an open-world issue. In the real world, people’s judgments on the possibility and uncertainty of events are mostly based on probability theory [ 2 ]. It is one of the most basic tools for dealing with uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Negation of Pythagorean Fuzzy Number Based on a New Uncertainty Measure Applied in a Service Supplier Selection System

Mao

Cai

2020

Entropy

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The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM) problem, it is also very difficult to measure the ambiguity degree of a set of PFN. A new entropy of PFN is proposed based on a technique for order of preference by similarity to ideal solution (Topsis) method of revised relative closeness index in this paper. To verify the new entropy with a good performance in uncertainty measure, a new Pythagorean fuzzy number negation approach is proposed. We develop the PFN negation and find the correlation of the uncertainty measure. Existing methods can only evaluate the ambiguity of a single PFN. The newly proposed method is suitable to systematically evaluate the uncertainty of PFN in Topsis. Nowadays, there are no uniform criteria for measuring service quality. It brings challenges to the future development of airlines. Therefore, grasping the future market trends leads to winning with advanced and high-quality services. Afterward, the applicability in the service supplier selection system with the new entropy is discussed to evaluate the service quality and measure uncertainty. Finally, the new PFN entropy is verified with a good ability in the last MCDM numerical example.

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Application of Bayesian Networks in Reliability Evaluation

Cited by 28 publications

References 93 publications

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

Remaining Useful Performance Estimation for Complex Analog Circuit Based on Maximal Information Coefficient and Bidirectional Gate Recurrent Unit

Negation of Pythagorean Fuzzy Number Based on a New Uncertainty Measure Applied in a Service Supplier Selection System

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