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

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 condit...

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Bibliographic Details
Published in:South African journal of industrial engineering 2021-11, Vol.32 (2), p.173-184
Main Authors: de Meyer, Jasper, Goosen, Pieter, van Rensburg, Johann, du Plessis, Johan, van Laar, Jean
Format: Article
Language:English
Subjects:
Breakdowns
Data analysis
Data transmission
Decision making
Electric currents
Investigations
Machinery
Maintenance management
Mining industry
Monitoring systems
Reliability analysis
Reliability aspects
Sensors
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Summary: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.
ISSN:1012-277X
2224-7890
DOI:10.7166/32-3-2625