Predictive Maintenance for Manufacturing

A manufacturing plant was losing $500K monthly due to unexpected equipment failures. We deployed IoT sensors + LSTM models to predict failures 48 hours in advance, reducing downtime by 85%.

Factory experiencing 20% unplanned downtime costing $500K monthly due to unexpected equipment failures.

• •Unexpected equipment failures causing production line shutdowns
• •Reactive maintenance approach leading to high costs
• •No visibility into equipment health
• •Average 20% unplanned downtime monthly

• Predict failures 24-48 hours in advance
• Reduce unplanned downtime by 60%+
• Lower maintenance costs by 30%+

Deployed IoT sensors + LSTM time-series model analyzing vibration, temperature, and performance metrics. Real-time alerts integrated with maintenance system.

Approach

• →Installed IoT sensors on 50+ critical machines
• →Trained LSTM models on vibration, temperature, pressure data
• →Built real-time monitoring dashboard
• →Integrated alerts with maintenance ticketing system