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Review
Engineering, Mechanical
Jie Yuan et al.
Summary: Oil and gas pipelines are crucial for fuel transportation, but leakages in them can result in serious consequences. This study fills the gap by conducting a comprehensive analysis of leak detection and localization techniques in the scientific literature. It includes statistical analysis of publications, institutions, and research interests in this field, proposes a classification method for these techniques, summarizes relevant standards, and provides a flowchart for selecting detection methods. Additionally, the study discusses the merits, limitations, and performance of different methods, and suggests potential directions for further improvements.
ENGINEERING FAILURE ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Guanxiang Hu et al.
Summary: Classification tasks are important in machine learning, but the problem of class imbalance can significantly affect classifier performance. This paper proposes a hierarchical belief rule-based system that integrates expert knowledge and utilizes extreme gradient boosting for feature selection to address class imbalance. By transforming multi-classification problems into binary classification problems and making precise predictions, class imbalance is alleviated.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Jing Liang et al.
Summary: Leak detection in natural gas pipelines is a significant and ongoing problem in the oil and gas industry. The complexity of pipeline construction, lack of closed-form solutions, and the need for experienced personnel limit the creation of accurate physical models. Additionally, industrial automation faces challenges with data overload and limited information. This paper proposes a data-driven digital twin (DT) method as a new solution to address these challenges. The proposed approach includes a DT pipeline learning and updating scheme based on operational data and a DT-driven leak detection method that utilizes data interaction and fusion. The effectiveness of the approach is demonstrated through a simulated leak scenario in a real running natural gas pipeline.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Wei He et al.
Summary: Modeling complex systems using only subjective judgment is insufficient. The expert knowledge in the belief rule base (BRB) is useful, but it has problems dealing with excessive combination rules caused by complex system attributes and losing interpretability of parameters. To address these issues, an interval construction belief rule base with interpretability for complex systems (IBRB-i) model is proposed. The IBRB-i model uses interval addition to create belief tables, adds rule reliability to prevent combination rule explosion, and incorporates interpretable constraints. The validity and precision of the IBRB-i model are analyzed and verified through case studies on power fault detection and liquid launch vehicle's structure safety assessment.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Chao Sun et al.
Summary: This paper proposes a novel BRB model with interval-valued references (BRB-IR), which integrates qualitative knowledge and quantitative data to construct models, and obtains optimal referential values through optimization algorithms. The results of a case study show that this new model is more effective and better characterizes expert knowledge than the traditional model with single-valued references.
SCIENTIFIC REPORTS
(2022)
Article
Automation & Control Systems
Shuai-Wen Tang et al.
Summary: In this article, a new ER rule-based safety assessment method with sensor reliability for complex systems is proposed, considering the uncertainties and perturbation of sensors. By calculating the static reliability and dynamic reliability, as well as employing the coefficient of the variation-based weighting method, the modeling accuracy and performance of safety assessment are improved.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Sumayh S. Aljameel et al.
Summary: Detection of minor leaks in oil or gas pipelines is a critical issue in the industry, and machine learning algorithms have been proposed to improve the accuracy and efficiency of leak detection.
Article
Computer Science, Artificial Intelligence
Fei Gao et al.
Summary: The proposed greedy-based BRB learning method aims to address the limitations of traditional BRB systems by selecting and reducing the optimal rules from candidate rules. Test results show that the method achieves relatively satisfactory accuracy in classifying benchmarks, outperforming some state-of-the-art classification methods when dealing with datasets with two or three classes.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Leilei Chang et al.
Summary: A new approach called NPMO-BRB is proposed to address problems with non-preferential multi-outputs, utilizing the belief rule base for superior nonlinearity modeling and explainability. Case study results demonstrate its superior performance, offering more information for practical work guidelines through belief distribution comparison.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
You Cao et al.
Summary: This article systematically summarizes the interpretability characteristics of the belief rule base (BRB) expert system and proposes four interpretability criteria to ensure its interpretability in optimization. An improved optimization algorithm with interpretability constraints derived from the criteria is developed to establish an interpretable BRB. A case study on the health state evaluation of aerospace relay demonstrates the effectiveness of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
You Cao et al.
Summary: Expert knowledge is key to the interpretability of BRB expert systems. To address the rule explosion problem and weak extendability of BRB, a new rule approximation method is proposed, leading to the development of the ABRB system. ABRB discounts attribute correlations using an independence factor and has theoretical proof of universal approximation ability.
DECISION SUPPORT SYSTEMS
(2021)
Article
Automation & Control Systems
Zhichao Feng et al.
Summary: A new safety assessment model based on BRB-r considering belief rule reliability is developed in this paper, achieving a good balance between complexity and accuracy.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Automation & Control Systems
Zhi-Jie Zhou et al.
Summary: The BRB model is an intelligent expert system that combines the features of expert systems and data-driven models, capable of handling various types of uncertain information and providing transparent decision support. It has been widely applied in fields such as decision making, reliability evaluation, and network security.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Electrochemistry
Xin Lai et al.
ELECTROCHIMICA ACTA
(2020)
Article
Computer Science, Artificial Intelligence
Anil Ramachandran et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
An Zhang et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2020)
Article
Energy & Fuels
Hao Fu et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2020)
Article
Construction & Building Technology
Rui Zhou et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2020)
Article
Engineering, Industrial
Y. Zhang et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2020)
Article
Engineering, Multidisciplinary
Jinhai Liu et al.
Article
Computer Science, Artificial Intelligence
Lei-Lei Chang et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2019)
Article
Automation & Control Systems
Chuang Wang et al.
SYSTEMS SCIENCE & CONTROL ENGINEERING
(2019)
Article
Engineering, Electrical & Electronic
Xianming Lang et al.
IEEE SENSORS JOURNAL
(2018)
Proceedings Paper
Automation & Control Systems
A. Lizarraga-Raygoza et al.
Article
Computer Science, Artificial Intelligence
Long-Hao Yang et al.
KNOWLEDGE-BASED SYSTEMS
(2017)
Article
Engineering, Environmental
Wenhe Wang et al.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2017)
Article
Computer Science, Artificial Intelligence
Dong-Ling Xu et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2007)
Article
Computer Science, Cybernetics
JB Yang et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
(2006)
Article
Engineering, Civil
J Rougier
JOURNAL OF HYDRAULIC RESEARCH
(2005)
