<![CDATA[Condition monitoring and early detection of machine failures are important aspects in industrial systems to improve operational reliability and reduce the risk of unexpected failures. This study aims to analyze the application of the Case-Based Reasoning (CBR) method in condition monitoring and early identification of machine failures by utilizing historical data and operational parameters. The variables used include temperature, vibration, pressure, rotational speed, and load as the main indicators of machine condition. The CBR method is implemented through four main stages, namely retrieve, reuse, revise, and retain. The retrieve stage is carried out by finding the most similar case using attribute-weighted similarity calculations, then the solution is adapted in the reuse stage, adjusted in the revise stage, and stored again in the retain stage to enrich the knowledge base. The calculation results show that the similarity value between the new case and the previous case reaches 0.948 (94.8%), which is obtained from a combination of local similarities of temperature (0.98), vibration (0.90), pressure (0.96), speed (0.99), and load (0.95) variables.]]>
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