Predictive Maintenance AI
IoT sensors and LSTM neural network implementation for predictive maintenance, reducing unplanned downtime by 70% and saving ৳2.8 crore annually.
Textile Manufacturing Company
Leading textile manufacturer with 100 critical production machines, operating 24/7 to meet global demand.
Unplanned Equipment Downtime
Unplanned equipment downtime was costing ৳15 lakhs per day, with reactive maintenance leading to production delays and high repair costs.
High Downtime Costs
Losing ৳15 lakhs daily due to unexpected equipment failures.
Reactive Maintenance
Fixing machines only after breakdown caused production delays.
Repair Costs
Emergency repairs cost 3x more than scheduled maintenance.
AI-Powered Predictive Maintenance
IoT sensors integrated with LSTM neural network for 7-day ahead failure prediction, enabling proactive maintenance scheduling.
IoT Sensor Network
Deployed vibration and temperature sensors across 100 critical machines.
Data Collection Pipeline
Real-time data streaming and storage infrastructure setup.
LSTM Neural Network
Deep learning model trained on historical failure patterns.
Real-Time Dashboard
Live equipment health monitoring and alerting system.
7-Day Prediction
Early warning system for potential equipment failures.
Proactive Maintenance
Automated work orders for scheduled repairs.
Key Results
Measurable outcomes that dramatically improved equipment reliability and reduced maintenance costs.
Real-Time Monitoring
Continuous equipment health monitoring
LSTM Neural Network
Advanced time-series prediction model
7-Day Prediction
Early failure detection system
Proactive Maintenance
Scheduled repairs before failures
Data-Driven Decisions
Analytics-based maintenance scheduling
Cost Optimization
Reduced repair and downtime costs
Technologies Used
Cutting-edge AI and IoT technologies for intelligent maintenance solutions.
"The AI-powered predictive maintenance system has transformed our operations. We can now anticipate equipment failures before they happen, saving us millions in unplanned downtime."