Ball bearings are among the most important and frequently encountered components in the vast majority of rotating machines, their carrying capacity and reliability being prominent for the overall machine performance. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. This paper presents fault detection of ball bearing using time domain features of vibration signals. The vibration signals are recorded at bearing housing of 5hp squirrel cage induction motor. These extracted features are used to train and test the neural network for four bearing conditions namely: Healthy, defective Outer race, defective Inner race and defective ball fault condition. The experimental observation shows that the proposed method is able to detect the faulty condition with high accuracy.
Yadav, Manish and Wadhwani, Dr. Sulochana
"Vibration analysis of bearing for fault detection using time domain features and neural network,"
International Journal of Applied Research in Mechanical Engineering: Vol. 1:
1, Article 14.
Available at: https://www.interscience.in/ijarme/vol1/iss1/14