European Journal of Engineering and Technology Research · 2019 · 27 citations · 0 references
ComputingComputer ScienceMechanical EngineeringData MiningMachine LearningVibrationDefect ClassificationDiagnosisDiagnosticsFault DetectionFault DiagnosisSupport Vector MachinesFault Detection Using Machine LearningFault Diagnosis In RotorsMachine Fault ClassificationMachine Learning-based Defect DetectionMachine Learning-based Fault DiagnosisRotor Imbalance DiagnosisSupport Vector ClassificationSupport Vector Machine Classification
This study proposes a method for diagnosing faults in turbomachines using machine learning techniques. In this study, a support vector machine-SVM algorithm is proposed for fault diagnosis of rotor rotation imbalance. Recently, support vector machines (SVMs) have become one of the most popular classification methods in vibration analysis technology. Axis unbalance defect is classified using support vector machines. The experimental data is derived from the turbomachine model of the rigid-shaft rotor and the flexible bearings, and the experimental setup for vibration analysis. Several situations of unbalance defects have been successfully detected.