Sugumaran, V.Jain, D.Amarnath, M.Kumar, H.2026-02-052013International Journal of Performability Engineering, 2013, 9, 2, pp. 221-2339731318https://idr.nitk.ac.in/handle/123456789/26864This paper uses vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using J48 decision tree algorithm. The paper also discusses the effect of various parameters on classification accuracy. © RAMS Consultants.Classification (of information)Computer aided diagnosisData miningFailure analysisFault detectionFeature SelectionLearning systemsVibrations (mechanical)Decision-tree algorithmEffect of featureFaults diagnosisFaulty conditionFeatures selectionGear fault diagnosisHelical gear boxMachine learning approachesStatistical featuresVibration signalDecision trees