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 production losses. Condition-based maintenance (CBM) is strategy that plans schedules depending on condition of equipment, aims improve decision-making processes. Data collected from for CBM purposes must be reliable avoid negative impacts strategy. reliability estimated by comparing multiple data streams; however, they are not always available, expensive. This study estimate isolated contextual single-source applying analytics techniques. An application designed analyse current machine level determine combined reliability. A case implementation shows difference in classification accuracy between methods, highlighting need them combined.