arnabroy734/machine_fault_detection
use of machine learning technique in predictive maintenance
This project helps operations and maintenance managers proactively identify potential machine faults before they cause breakdowns. It takes real-time sensor data from machinery, such as pressure, temperature, and current readings, and outputs alerts about developing anomalies that indicate an impending fault. This allows maintenance teams to schedule interventions rather than reacting to failures.
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Use this if you need to predict equipment failures using continuous sensor data to transition from reactive to predictive maintenance.
Not ideal if your historical data lacks examples of faults or if you cannot collect continuous, high-frequency sensor readings from your machines.
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Last pushed
Aug 18, 2023
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