Qing Xia¹, Yonghua Li^2,*, Dongxu Zhang², Yufeng Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724 - 17 November 2023

Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network… More >

Xiaoyu Cheng¹, Mingxian Long¹, Wei He^1,2,*, Hailong Zhu¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2821-2844, 2023, DOI:10.32604/csse.2023.037330 - 03 April 2023

Abstract Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. More >

Seyed Mohammad Seyed Hosseini^*, Kamran Shahanaghi, Safar Shasfand

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 815-833, 2022, DOI:10.32604/fdmp.2022.019626 - 22 February 2022

Abstract

The thermo-economic performance of a gas turbine is simulated using a fish bone technique to characterize the major equipment failure causes. Moreover a fault tree analysis and a Pareto technique are implemented to identify the related failure modes, and the percentage and frequency of failures, respectively. A pump 101 and drier 301 belonging to the Tabriz Petrochemical Company are considered for such analysis, which is complemented with a regression method to determine a behavioral model of this equipment over a twenty-year period. Research findings indicate that 81% of major failure factors in production equipment are related

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Rongxing Duan¹, Yanni Lin¹, Longfei Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.1, pp. 17-34, 2017, DOI:10.3970/cmes.2017.113.015

Abstract Fault tolerant technology has greatly improved the reliability of modern systems on one hand and makes their failure mechanisms more complex on the other. The characteristics of dynamics of failure, diversity of distribution and epistemic uncertainty always exist in these systems, which increase the challenges in the reliability assessment of these systems significantly. This paper presents a novel reliability analysis framework for complex systems within which the failure rates of components are expressed in interval numbers. Specifically, it uses a dynamic fault tree (DFT) to model the dynamic fault behaviors and copes with the epistemic More >