Edge Based Machine Learning Improves OEE
Anomaly Detection and Root Cause Analysis
Condition Monitoring · Predictive Maintenance · Operational Intelligence · Asset Performance
Bad bearings
Bent shaft
Wrong speeds
Optimize energy
Cracked rotor bar
Electrical defects
Impeller Wear
Find best efficiency point
Cavitation
Leaks
Resonance and flow
Turbulence
Belt problems
Lubrication quality
Coupling misalignment
Rotor unbalanced
Crosshead looseness
Seal wear
Tooth wear
Excessive load
Broken or cracked teeth
Bearing wear
Good shoot
Bad shoot
Optimal valve operation
Out of balance
Excess load
Temperatures
Humidity
Mold prevention
Fire/explosive prevention
Storage levels
Temperatures
Fire/explosive prevention
Insulator faults
Wire faults
Part quality
Part inspection
Package inspection
Process validation
Safety validation
Geo-fencing
Security
Authorization
Access control
Make every sensor, machine, process and product smarter and better.
Latency, Data Loads and Context Often Demand ML on the Edge
FogLAMP AI integrates ML life-cycle with OT-IT:
- OT knowledge of the machines and processes (build and improve models continuously)
- The collection and labeling of data
- Model deployment
- Model runtime notifications and alerts with existing OT-IT systems
- Choice of ML/AI tools
FogLAMP AI enables ML edge lifecycles. Start with labeling and harmonizing data, build models, distribute models, execute models and alert all on the edge.
