Aerospace Predictive Maintenance
Unplanned Downtime Crisis
An aerospace manufacturer was experiencing costly unplanned equipment downtime, with each incident causing production delays worth millions of dollars and risking delivery commitments.
Key Pain Points
- Average $3M cost per unplanned downtime incident
- Reactive maintenance causing production disruptions
- Difficulty predicting component failures
- Complex equipment with thousands of parameters
- Limited visibility into equipment health trends
AI-Driven Predictive Maintenance
Tesan AI deployed comprehensive predictive maintenance using digital twins and advanced AI algorithms to predict failures before they occur.
Key Features Deployed
- Digital twin creation for critical equipment
- Real-time sensor data integration
- AI-powered failure prediction models
- Automated maintenance scheduling
- Root cause analysis automation
- Parts inventory optimization
Operational Excellence
Measurable outcomes from the implementation
Implementation Journey
A structured approach to transformation
Equipment Analysis
Identification of critical assets and failure modes
Sensor Integration
IoT sensor deployment and data pipeline setup
Model Development
AI model training using historical failure data
Digital Twin Creation
High-fidelity simulation models for key equipment
Operational Integration
Integration with CMMS and production planning
Use Cases Implemented
Specific applications within this implementation
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