Yan‐bin Yuan, Ya‐qiong Zhu, You Zhou, N.R. Sælthun, Wei Cui and Jiejun Huang
The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the…
Abstract
Purpose
The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the inherent relation between mineral information genes and metallogenic probability, and offer the scientific basis for target prediction.
Design/methodology/approach
Mineral information includes all kinds of relative metallogenic information. In order to extract comprehensive metallogenic prediction information, it is necessary to filter initial observation information to emphasize the factors that are most advantageous to metallogenic prognosis. Rough set can delete irrespective or unimportant attributes on the premises of no information missing and no classification ability changing, without supplementary information or prior knowledge, which has important theoretic and practical value for metallogenic prognosis.
Findings
The association and importance of geological information referring to prospecting are found out through attribute reduction based on rough set.
Originality/value
The analysis of geological and mineral information based on rough set is a novel approach for high‐dimensional complex non‐deterministic polynomial problems which are predominant in geological research. The research successfully extracts characterized mineralization information to offer the scientific basis for target prediction.
Details
Keywords
Rongxing Duan, Shujuan Huang and Jiejun He
This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an…
Abstract
Purpose
This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail.
Design/methodology/approach
First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency.
Findings
In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis.
Originality/value
The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.