This study addresses prognostics and health management (PHM) for manufacturing machines. Different from previous researches where continuous monitoring is assumed for PHM, we investigate the issue with discrete event data. Various event data are recorded during system operation, which can provide useful information for fault diagnosis and failure prediction. We focus on discovery of association rules based on the industrial discrete data. Apriori algorithm is employed to discover the frequent event groups and identify strong association rules. To accommodate the algorithm, the initial event data is transformed into the form of transactional data as a first step. The obtained association rule estimates the occurrence probability of certain significant events within specified time interval. It is concluded through a case study that the number of frequent event groups and strong association rules increases with the time interval that the events are grouped as one transaction.
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Prognostics and Health Management, Discrete event data, Association rules, Data mining, Manufacturing machines
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