Maintenance Management of Mining Belt Conveyor System Based on Data Fusion and Advanced Analytics

Abstract

Belt conveyor network is an important transportation form used in the underground copper ore mines. Effective maintenance of this infrastructure is critical–serious failure of single conveyor might stop operation of several conveyors connected in series and finally might affect production volume. To achieve expected reliability, one should use appropriate tools for supporting maintenance management and decision-making process. Nowadays, predictive maintenance seems to be the most powerful approach for industrial applications. Deep understanding design and operational factors, knowledge about of repairs (number, type, reasons, etc.), and finally acquisition and processing of appropriate physical variables might provide suitable information for maintenance staff. However, mining industry, especially underground mine is a specific kind of factory. Harsh environmental conditions (high humidity, temperature, dust, etc.), varying load, specific damage scenarios make practical implementation of predictive maintenance difficult. Also a scale of transportation system plays an important role: >80 conveyors with different configurations (1–4 drives), diversity of dimensions (short . long conveyors), locations (environmental issues, operation on the slope), etc. All these facts required special approach based on diagnostic data acquisition, necessary processing and context-based reasoning. In this paper, we will discuss details related to development of analytical IT-based environment integrating data from many different sources and procedures supporting decision-making process. We will propose the concept of Decision Support System for maintenance of conveyors system. Because of the multidimensional nature of diagnostic data and diversified technical configurations of the facilities, it was necessary to develop and implement multivariate analytical models including data fusion and artificial intelligence techniques. Consequently, it allows to avoid failures, supports scheduling repairs, and finally provides reduction of repairs costs and production losses related to breakdowns.