Step	Description	Output
1. Planning & Objectives	Identified asset health goals and maintenance KPIs with operations team.	Clear KPIs for downtime, failure rates, and maintenance costs.
2. Data Collection	IoT sensor data (vibration, temperature, etc.) Maintenance logs & historical failure data Usage patterns and production data	Comprehensive dataset capturing asset health indicators.
3. Data Cleaning & Preparation	Removed noisy/outlier data Created features for failure prediction	Reliable dataset for predictive modeling.
4. Model Building	Developed predictive models to forecast asset failures and maintenance needs.	Predictive model estimating equipment failure likelihood.
5. Visualization & Insights	Dashboards with health scores and risk indicators Visual maintenance schedules	Actionable insights for preventive maintenance planning.
6. Testing & Validation	Model accuracy validation Stakeholder reviews and refinements	Reliable, actionable maintenance insights.
Final Output	Fully functional predictive maintenance system integrated with operations dashboards.	Reduced downtime by 25% Lower maintenance costs Improved asset performance