HarshGupta-DS/Predictive-Maintainence-using-Data-Analysis-and-Time-Series-Forecasting
Predictive Maintenance avoids the drawbacks of Preventive Maintenance (under utilization of a part's life) and Reactive Maintenance (unscheduled downtime). Based on the health of an equipment in the past, future point of failure can be predicted in Predictive Maintenance. Thus, replacement of parts can be scheduled just before the actual failure.
This project helps operations managers and maintenance engineers prevent unexpected equipment breakdowns. By analyzing historical sensor data, error logs, and maintenance records from machinery, it predicts when a machine component is likely to fail. This allows for proactive scheduling of part replacements, minimizing costly downtime and improving equipment reliability.
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Use this if you manage industrial equipment and want to shift from reactive or time-based maintenance to a more efficient, data-driven predictive approach.
Not ideal if your equipment lacks sensor data or if failures are primarily due to external, unpredictable events rather than internal wear and tear.
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License
MIT
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Last pushed
Apr 25, 2022
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