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Article

Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis

1
School of Civil Engineering, Architecture & Environment, Hubei University of Technology, Wuhan 430068, China
2
Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan 430068, China
3
SINOHYDRO BUREAU 3 Co., LTD POWERCHINA, Xi’an 710024, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(11), 4745; https://doi.org/10.3390/app14114745
Submission received: 24 April 2024 / Revised: 26 May 2024 / Accepted: 28 May 2024 / Published: 30 May 2024
(This article belongs to the Section Civil Engineering)

Abstract

A concrete production system (CPS) fault in dam engineering is one of the important factors influencing dam construction quality, which may directly affect the concrete-pouring construction progress and construction efficiency of the dam, and can even cause construction quality defects in the dam body. Reasonable classification and identification are of great significance to ensure the construction progress and quality of concrete dams. In this study, based on the concrete production logs of multiple concrete dams and literature reviews, a fault classification system for a CPS is proposed by comprehensively considering its mechanical structure characteristics and operating characteristics. The faults of the CPS are divided into 4 large categories and 22 subcategories. Additionally, the causes of CPS faults are summarized as human factors, environmental factors, mechanical component service life factors, and other factors. Based on the grey correlation analysis (GCA) method, a fault identification model of the CPS is established. With the actual production system fault statistical data of Shatuo hydropower station, the correlation coefficients for the four types of faults and the four influencing factors are calculated to determine the key faults of the CPS. The research results of the case study show that the service life factors of mechanical components have the greatest impact on batching metering system faults and mixer faults, with high grey correlation degrees of 84.66% and 76.85%, respectively. Environmental factors have the greatest impact on material delivery system faults and pneumatic system faults, with high grey correlation degrees of 90.81% and 94.9%, respectively. This paper provides theoretical support for the realization of fault pattern recognition of CPSs and provides a guiding reference for targeted fault handling.
Keywords: CPS fault; fault classification; grey correlation analysis; fault identification model CPS fault; fault classification; grey correlation analysis; fault identification model

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MDPI and ACS Style

Zhou, H.; Mi, T.; Zhao, C.; Liang, Z.; Fang, T.; Wang, F.; Zhou, Y. Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis. Appl. Sci. 2024, 14, 4745. https://doi.org/10.3390/app14114745

AMA Style

Zhou H, Mi T, Zhao C, Liang Z, Fang T, Wang F, Zhou Y. Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis. Applied Sciences. 2024; 14(11):4745. https://doi.org/10.3390/app14114745

Chicago/Turabian Style

Zhou, Huawei, Tonghao Mi, Chunju Zhao, Zhipeng Liang, Tao Fang, Fang Wang, and Yihong Zhou. 2024. "Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis" Applied Sciences 14, no. 11: 4745. https://doi.org/10.3390/app14114745

APA Style

Zhou, H., Mi, T., Zhao, C., Liang, Z., Fang, T., Wang, F., & Zhou, Y. (2024). Identification Model of Fault-Influencing Factors for Dam Concrete Production System Based on Grey Correlation Analysis. Applied Sciences, 14(11), 4745. https://doi.org/10.3390/app14114745

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