Cloud model-clustering analysis based evaluation for ventilation system of underground metal mine in alpine region

Abstract

Ventilation system is significant in underground metal mine of alpine region. Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system. However, it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region. This paper proposes a novel evaluation method called the cloud model-clustering analysis (CMCA). Cloud model (CM) is utilized to process collected data of ventilation system, and they are converted into cloud descriptors by CM. Cloud similarity (CS) based Euclidean distance (ED) is proposed to make clustering analysis of assessed samples. Then the classification of assessed samples will be identified by clustering analysis results. A case study is developed based on CMCA. Evaluation results show that ventilation effectiveness can be well classified. Moreover, CM is used alone to make comparison of evaluation results obtained by CMCA. Then the availability and validity of CMCA is verified. Meanwhile, difference of CS based ED and classical ED is analyzed. Two new clustering analysis methods are introduced to make comparison with CMCA. Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.