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One of the key issues encountered in development of condition monitoring systems for industry is definition of decision rules in diagnostic system for determined diagnostic features. In practice, it appears very often that proposed algorithm is not effective for all technical assets of machinery park. The major cause is usually related to smaller or higher diversity of objects, mainly in terms of design features, operating conditions and wear level. These factors directly influence the profile of measured vibration signals, diagnostic features, thresholds, decision rules and so on. In this paper authors propose the usage of Generalized Rule Induction (GRI) algorithm for association rules discovery from data base of the Computerized Maintenance Management System (CMMS) - patterns hidden in data reflecting existing processes phenomena, regularities, and expresses relationships between them. Such approach provides better interpretation of signals, and consequently, much more effective decision rules.

Association rules discovery from diagnostic data-application to gearboxes used in mining industry