AI Agent running locally or on secure cloud infrastructure to monitor machine health, schedule maintenance, and optimize production workflows. This can be considered a back office solution that continuously collects and analyzes production data, communicating alerts and suggestions to operators in real time.
Key Features
- Machine Health Monitoring
- Integrates with sensors (vibration, temperature, energy consumption) on the production line.
- Uses anomaly detection and ML models to spot early warning signs of equipment failure.
- Predictive Maintenance Scheduling
- Dynamically assigns maintenance tasks when certain thresholds or patterns appear, minimizing unplanned downtime.
- Balances maintenance windows with production targets to avoid bottlenecks.
- Workflow & Resource Allocation
- Evaluates production throughput, identifies idle or overloaded stations.
- Automatically suggests workload shifts or adjusted staffing to keep the line running efficiently.
- Real-Time Alerts & Dashboards
- Provides operators with alerts on potential breakdowns or slowdowns.
- Visual dashboards display current OEE (Overall Equipment Effectiveness), upcoming maintenance tasks, and process bottlenecks.
Example Workflow
- Data Collection: Sensors track motor vibration and temperature on a conveyor belt.
- Agent Analysis: The agent notices a slow but steady rise in vibration amplitude, historically correlated with bearing wear.
- Alert & Action: The system flags the machine for maintenance within 48 hours and checks staff schedules to find the best downtime slot.
- Follow-Up: Maintenance is performed. The agent logs the event and updates the machine’s health profile for future reference.
Impact
- Minimized Downtime: Early detection of issues prevents catastrophic machine failures.
- Optimized Throughput: Balances workloads, reducing bottlenecks and maximizing capacity.
- Data-Driven Decisions: Real-time analytics enable fast, informed actions by both operators and managers.