Thermal humidity risk assessment in high-temperature environment of mines based on uncertainty measurement theory

Shu, Hongmei; Li, Naiping; Dong, Longjun; Luo, Qiaomu; Sabao, Ashley Ruvimbo

doi:10.1016/j.csite.2023.103401

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…Ye et al [26] established a safety evaluation index system for underground ventilation systems in uranium mines based on AHP and fuzzy comprehensive assessment. To help mine management assess the risk of scaring mining in extremely hot and humid environments, Shu et al [27] proposed a multi-indicator hierarchical model. The entropy weighting method was used to determine the index weights and derive the multi-index measure evaluation matrix.…”

Section: Introductionmentioning

confidence: 99%

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

Li,

Zhang,

Fang

et al. 2024

Mathematical Problems in Engineering

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As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems.

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Section: Introductionmentioning

confidence: 99%