Utilisation of Data Mining in Mining Industry: Improvement of the Shearer Loader Productivity in Underground Mines
This paper presents an investigative study in which data were gathered and used in underground mining to improve the planned maintenance program and reliability of the shearer loader in the underground mining. A cost effective maintenance and operation strategy and practices is mandatory to meet the production demand and the required level of service of this critical asset of the plant. The study conducted and presented in this paper includes a detailed review of failure history data and the use of analytical technique available to understand failure characteristics and its effect on the throughput and the overall performance of the longwall operation. This is to achieve further productivity increases to meet business goals. Analytical tools such as Failure Mode and Effect and Criticality Analysis (FMECA) and Weibull analysis were used in this investigation. The study has highlighted the criticality of some failures and the actions needed in this industrial case to improve the reliability and planned maintenance for a better mining productivity.
Data Mining and Analysis Mining Industry Reliability Centred Maintenance (RCM) Total Productive Maintenance (TPM) Planned Maintenance Optimisation (PMO) Root Cause Analysis (RCA) FMECA Risk Priority Number Human Reliability
Benhur Balaba M. Yousef Ibrahim Indra Gunawan
School of Applied Sciences and EngineeringMonash University, Gippsland CampusChurchill, VIC 3842, Au School of Applied Sciences and Engineering Monash University, Gippsland Campus Churchill, VIC 3842,
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
1041-1046
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）