Establishment of the best maintenance practices for optimal reconfigurable vibrating screen management using decision techniques

Abstract

Purpose Reconfigurable vibrating screen (RVS) is an innovative beneficiation machine designed at Tshwane University of Technology, Republic of South Africa (RSA); with adjustable screen structure to ensure sorting, sizing and screening of varying mineral particles (sizes and quantities) demanded by the customers in a cost-effective manner through the screen structure geometric transformation. In order to ensure that this machine is optimally maintained and managed when utilized in surface and underground mining industries, there is a need to establish or ascertain the best maintenance practices that would be used in optimally managing the RVS machine using decision making techniques. In view of this, the purpose of this paper is to ascertain the best maintenance practices that would be used to optimally maintain and manage the RVS machine when used in surface and underground mines. Design/methodology/approach Decision making techniques such as weighted decision matrix (WDM) and analytical hierarchy process (AHP) were used in this research work to establish the best maintenance practice for optimally maintaining and managing the RVS machine using relevant literature survey on maintenance management systems as well as the different maintenance criteria decision indices obtained from different conventional vibrating screen machine manufacturers and maintenance experts. Findings Based on the results obtained from the WDM analysis, it was anticipated that e-maintenance (e-M) system embedded with diagnosing and prognosing algorithms; with a cumulative weight score of 2.37 is the best maintenance practice for managing the RVS machine when used in surface mines, while AHP with deeper decision making analysis anticipated that the robotic-driven maintenance (RM) system with an important decision criteria; safety, and a cumulative hierarchy score of 28.6 percent, supported by e-M management system with a cumulative hierarchy score of 17.6 percent are the best maintenance mix that could be used in optimally maintaining and managing the RVS machine, when used in a craggy and hazardous underground mining environment. Practical implications To this effect, it could be anticipated that e-M management system (endowed with the ability to detect fault on the machine, diagnose and prognose the different subsystems of the RVS machine and ascertain the reconfiguration time and process of the RVS machine in recovering production loss during the maintenance of the machine as well as meeting customers demand, etc.) is the best maintenance practice for optimally maintaining the RVS machine when utilized in surface mines while both e-M management system and RM management system (endowed with the ability to carry out automated maintenance tasks achievement under little or no maintenance manager intervention) are also anticipated as the best customized maintenance practices mix that could be used in optimally maintaining the RVS machine, when used in dangerous and hazardous underground mining environment. Originality/value This maintenance management system evaluation and selection for optimal RVS machine functionality will serve as a useful information to different mining machines (and other related machines) maintenance managers, in selecting the best maintenance management system for ensuring optimal functionality, reliability and maintainability of machines used in their industries.

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