A predictive maintenance system that uses machine learning to predict equipment failures and schedule maintenance. This system helps businesses reduce downtime and maintenance costs by identifying potential issues before they become critical. The system is built using Python and employs Scikit-learn for the machine learning models. Pandas and NumPy are used for data manipulation and analysis. The system analyzes historical data from equipment sensors to identify patterns and predict future failures. The predictive maintenance system includes a dashboard for monitoring equipment status and viewing maintenance predictions. It can be integrated with existing maintenance management systems to automate the scheduling of maintenance tasks. The system also provides alerts and notifications to ensure that maintenance activities are carried out on time.
This project involves the development of a predictive maintenance system that uses machine learning algorithms to predict equipment failures and schedule maintenance activities, reducing downtime and costs.