Abstract
Rule based diagnosis is a machine condition diagnostic technology, and the result obtain through its responses to attributes consists of expert knowledge and experience. Accordingly, unlike machine learning, data and general-purpose aspects have advantages as they do not require big data and learning. However, rule-based diagnosis requires the user to respond to attributes, resulting in individual errors or time costs. Hence, it needs to be performed automatically. This paper develops a signal recognition technique by analyzing the diagnostic parameters of the rule-based diagnostic attributes. The diagnostic parameter consists of two characteristics and is recognized using different techniques. It is based on signal recognition, confirming its diagnostic potential, Further, this study is expected to enhance the automation of this diagnosis.
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