judithspd/predictive-maintenance
Baseline study on the development of predictive maintenance techniques using open data. Two problems are discussed: classifying a vibration signal as healthy or faulty and on the other hand, given a signal predicting time to failure based on early anomaly detection.
This project helps maintenance engineers and operations managers diagnose potential equipment issues from vibration signals. It takes raw vibration data from machinery and classifies the signal as healthy or faulty, identifies the specific type of failure, and predicts how much time is left before a critical failure occurs. This is ideal for professionals managing industrial equipment and seeking to move from reactive to proactive maintenance.
No commits in the last 6 months.
Use this if you need to automatically assess machine health, classify different types of equipment failures, and forecast remaining useful life based on vibration sensor data.
Not ideal if you are looking for a complete, production-ready predictive maintenance system rather than a foundational study and baseline models.
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21
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8
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 10, 2021
Commits (30d)
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