Title: Condition Monitoring of Ball Bearing Having Defect at Inner Race Using Vibration Analysis and Machine Learning

Authors: Pallavi Khaire,V. M. Phalle

DOI: 10.33599/nasampe/s.22.0860

Abstract: The rotating machine comprises of numerous components such as shaft and bearing. The overall performance of machine is dependent on the health of these components. Vibration analysis is an effective tool to identify these faults. A methodology for ball bearings fault diagnosis using Artificial Neural Network and Decision Tree classifier is presented in this paper. The Finite Element analysis is carried out using ANSYS for a healthy bearing and a bearing having fault at inner race. The experimental vibration data for healthy and faulty bearing is acquired using FFT analyzer. Finally, after bearing faults classification using statistical feature extraction, the data input is fed to machine learning algorithm. Two machine learning techniques are used for faults classifications, i.e., Artificial Neural Network (ANN) and Decision Tree Classifier (DT). The simulation data is used for training purpose whereas the experimental data is used for testing purpose. It is observed that Ball Pass Frequency at inner Race is the indication of fault. The simulation and experimental results are in close agreement with the literature available. The proposed model of machine learning is able to identify rolling element bearing faults. The accuracies of ANN model and DT classifier model are 87% and 89% respectively.

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Conference: SAMPE 2022

Publication Date: 2022/05/23

SKU: TP22-0000000860

Pages: 15

Price: $30.00