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The data generated within the construction industry has become increasingly overwhelming. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This study uses the C5.0 DT algorithm which is an improved version of C4.5 and ID3 algorithms to diagnose the machine status and detect the failure of unhealthy machines in vibration signals database which contained of vibration amplitude and frequencies at three planes (horizontal, vertical and axial) of centrifugal pumps. In addition the result compared with some popular classifiers like QUEST, CART, CHAID to evaluate the result and showed the C5.0 performance is remarkably better than others. In this paper C5.0 identifies all three classes of machine status with 92.08% accuracy in test subset and furthermore it detects 4 failures classes of 5 with 73.2 accuracy.
Date of Conference: 2-4 Nov. 2010