HassanMahmoodKhan/Machine-Learning-Based-Fault-Diagnosis-of-Electric-Drives
Fault diagnosis of some critical and non-critical faults in electric drives using anomaly detection.
This project helps operations engineers and maintenance staff automatically identify common electrical and mechanical faults in AC electric drives. It takes time-series data from motor output parameters and classifies it as one of several fault types (like over voltage or overloading) or normal operation. This allows for quick and accurate diagnosis to prevent costly downtime and ensure safety.
No commits in the last 6 months.
Use this if you need to rapidly and accurately diagnose faults in industrial AC electric drives to proactively schedule maintenance.
Not ideal if you are working with different types of motors or systems beyond AC electric drives, or if your primary goal is anomaly detection without specific fault classification.
Stars
13
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2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 21, 2023
Commits (30d)
0
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