ShubhankarKG/RUL_Prediction_SVM
Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model - Implementation of Research Paper : https://doi.org/10.1016/j.isatra.2019.08.058
This project helps maintenance engineers and operations managers predict when ball bearings in industrial machinery are likely to fail. By analyzing vibration and degradation data from operating bearings, it provides an estimate of their Remaining Useful Life (RUL). This allows for proactive maintenance planning, reducing unexpected downtime and operational costs.
No commits in the last 6 months.
Use this if you need a reliable way to forecast the degradation of industrial bearings to schedule maintenance before critical failures occur.
Not ideal if you are looking to predict failures for components other than ball bearings or require real-time, high-frequency anomaly detection rather than life-cycle prediction.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Nov 10, 2021
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