ESTIMATING THE RELIABILITY OF CONDITION-BASED MAINTENANCE DATA USING CONTEXTUAL MACHINE-SPECIFIC CHARACTERISTICS

被引：0

作者：

de Meyer, J. N. ^{[1
]}

Goosen, P. ^{[1
]}

van Rensburg, J. F. ^{[1
]}

du Plessis, J. N. ^{[2
]}

van Laar, J. H. ^{[2
]}

机构：

[1] North West Univ, Ctr Res & Continued Engn Dev CRCED, Potchefstroom, South Africa

[2] Stellenbosch Univ, Dept Ind Engn, Stellenbosch, South Africa

来源：

SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING | 2021年 / 32卷 / 03期

关键词：

Reliability - Data Analytics - Decision making - Machinery;

D O I：

10.7166/32-3-2625

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In the mining industry, inter-connected machinery operates under harsh conditions 24 hours a day. Naturally, this degrades their state, and can lead to premature breakdowns and production losses. Condition-based maintenance (CBM) is a strategy that plans maintenance schedules depending on the condition of the equipment, and aims to improve decision-making processes. Data collected from machinery for CBM purposes must be reliable to avoid negative impacts on the maintenance strategy. Data reliability can be estimated by comparing multiple data streams; however, they are not always available, and can be expensive. This study aims to estimate the isolated and contextual reliability of single-source CBM data by applying multiple data analytics techniques. An application is designed to analyse current data on a machine level and to determine combined reliability. A case study implementation shows the difference in reliability classification accuracy between the isolated and contextual methods, highlighting the need for them to be combined.

引用

页码：173 / 184

页数：12