所蔵: Vibration Signal for Bearing Fault Detection using Random Forest

Vibration Signal for Bearing Fault Detection using Random Forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary ob...

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主要な著者:	Abedin T., Koh S.P., Yaw C.T., Phing C.C., Tiong S.K., Tan J.D., Ali K., Kadirgama K., Benedict F.
その他の著者:	57226667845
フォーマット:	Conference Paper
出版事項:	Institute of Physics 2024
主題:	Bearing Fault Detection Principal Component Analysis Random Forest Classification (of information) Fault detection Forestry Induction motors Learning systems Vibration analysis Bearing fault detection Data frames Faults detection Outer races Principal-component analysis Property Random forest classifier Random forests Vibration signal Principal component analysis
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Vibration Signal for Bearing Fault Detection using Random Forest

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