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A machine learning project for classifying bearing faults using the CWRU dataset, with models built using Python and various ML techniques such as cross-validation, PCA, tSNE, SVM, XGBoost.
Fault Bearing Classification Analysis dashboard to explore, diagnose and highlight potential factors to predict the fault class based on bearing statistical manufacturing data.
This research is conducted as part of the NSBE Aerospace SIG internship program. It is focused on investigating The Feasibility of Implementing Predictive maintenance on Rotorcraft Health and Usage Monitoring Systems.
the PLS allows both to classify the types of faults and to reduce the dimensionality of the problem by trying to maximize the covariance between X and Y, useful in supervised learning.
Showcase how machine learning can help plant operator monitor equipment condition through correctly analyzing measurement data collected from many sensors.