waibhav-jha/Ball-Bearing-Fault-Prediction-using-SVM-and-NTK
This project explores fault detection in ball bearings using Support Vector Machines (SVM) with Neural Tangent Kernel (NTK). By leveraging advanced machine learning techniques on vibration signal data, we achieve high-accuracy predictive maintenance, helping to prevent machine failures and optimize industrial operations.
This project helps industrial operations managers and maintenance engineers predict ball bearing failures before they happen. By analyzing vibration signal data from machinery, it identifies potential faults, providing clear insights into the health of critical equipment. The output helps prioritize maintenance tasks to prevent costly breakdowns.
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Use this if you need to reliably predict ball bearing faults from vibration data to optimize maintenance schedules and minimize unexpected downtime.
Not ideal if you are looking for a general-purpose anomaly detection tool not specific to machine fault prediction or if your data is not vibration signals.
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Feb 19, 2025
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