RuslanBabudzhan/QuantumBearings
Detection of defective rolling bearings with machine learning methods based on bearings acceleration data
This project helps operations engineers and maintenance teams automatically detect defective rolling bearings. It takes raw acceleration data from bearing vibration sensors and uses machine learning to classify whether a bearing is healthy or has a defect. This allows for proactive maintenance and reduces unexpected equipment failures.
No commits in the last 6 months.
Use this if you need to identify faulty bearings in machinery based on vibration data to prevent downtime and optimize maintenance schedules.
Not ideal if you require real-time, highly specialized fault diagnostics for very complex, multi-component systems beyond simple bearing defect detection.
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Last pushed
Dec 02, 2021
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