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.
No commits in the last 6 months.
Stars
3
Forks
—
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 19, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/waibhav-jha/Ball-Bearing-Fault-Prediction-using-SVM-and-NTK"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
petrobras/BibMon
Python package that provides predictive models for fault detection, soft sensing, and process...
hustcxl/Deep-learning-in-PHM
Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
kokikwbt/predictive-maintenance
Datasets for Predictive Maintenance
biswajitsahoo1111/rul_codes_open
This repository contains code that implement common machine learning algorithms for remaining...
tvhahn/weibull-knowledge-informed-ml
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets....