Investigation and Minimization of the Vibrations in High-Precision Manufacturing using Machine Learning

The work focuses on fault diagnosis and categorization of rotating equipment parts, using Machine Learning approaches based on the vibration signals pulled from the CWRU Bearing Data Centre at a sampling rate of 48000 samples/sec. The research uses multiple machine learning models, including Support Vector Machines (SVM), 1D Convolution Neural Networks (CNN), 2D CNN, and Long Short-Term Memory (LSTM) networks. In order to minimize overfitting and provide an accurate performance assessment, we used the concept of k-fold cross-validation. Our findings proved the resilience of Machine Learning models for predictive maintenance to detect and identify the faults in the bearings at an early stage. This results in cutting down the frequency of downtime, cost of maintenance, and extending the lifespan of the bearings, so demonstrating the applicability of using Machine Learning in enhancing functionality and monitoring future structures in High-Precision Manufacturing Industries.