A Bayesian Belief Network Approach to Belt Conveyor System Monitoring
نویسنده
چکیده
To optimize belt conveyor system (BCS) performance, a reasonable assessment of system reliability is desired and the decisions of how often to perform maintenance and the optimal occasion to replace a component are required. Intelligent belt conveyor monitoring (BCM) may automate the maintenance process. To realize such an intelligent system most of parameters of BCS have to be monitored. Too many monitored parameters during operation phases cause system complexity. This paper presents a Bayesian Belief Network (BBN) approach to BCM to predict failures, discover component fault causes, evaluate the system reliability and decide on maintenance strategies. The BBN model encodes probabilistic dependencies among monitored working variables into a directed acyclic graph of Bayesian causal structure. Reasoning of such a BBN results in alarms of abnormality of monitored variables, maintenance and replacement decisions for degraded components, optimal operational strategies of system functioning and evaluation of system condition.
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