Vibration Signal for Bearing Fault Detection using Random Forest

• Main Authors: Abedin T., Koh S.P., Yaw C.T., Phing C.C., Tiong S.K., Tan J.D., Ali K., Kadirgama K., Benedict F.
• Other Authors: 57226667845
• Format: Conference Paper
• Published: Institute of Physics 2024
• Subjects: 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
• Institution: Universiti Tenaga Nasional

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...