This project develops a predictive maintenance tool for industries such as construction, telecom, and utilities, which rely on heavy equipment like generators, compressors, and tower lights. The tool aims to minimize costly downtime, project delays, and emergency repair costs by forecasting maintenance needs based on historical data. Predictive Maintenance Tool for Heavy Equipment Overview This project develops a predictive maintenance tool for industries such as construction, telecom, and utilities, which rely on heavy equipment like generators, compressors, and tower lights. The tool aims to minimize costly downtime, project delays, and emergency repair costs by forecasting maintenance needs based on historical data. Problem Statement Traditional reactive maintenance (fixing equipment after failure) leads to:
Costly downtime Delayed project timelines Increased emergency repair costs
✨ Key Features Usage Monitor: Visualize daily/monthly usage trends
Maintenance Forecast: Predict next maintenance date
Flag high-risk equipment
Dashboard Activation: To run the project, type 'streamlit run app.py' in the terminal
Failure Risk Model:
Predict likelihood of failure in next 30/60/90 days
KPI Dashboard:
MTBF (Mean Time Between Failures)
Average downtime
Cost of repairs by equipment type
Interactive Filters: Search by asset type, location, severity level