Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach
摘要
Earlier studies have optimised the vibration-based parameters to identify the rotor defects only for the rotating machines. The artificial neural network (ANN) model was used earlier to classify the faults. The earlier optimised parameters are further examined for both rotor and bearing defects. These parameters are slightly modified in this research to accommodate bearing defects. The paper presents the study using an experimental vibration data from a laboratory-scaled rig.