FogLAMP AI

Edge Based Machine Learning Improves OEE

Anomaly Detection and Root Cause Analysis

Condition Monitoring · Predictive Maintenance · Operational Intelligence · Asset Performance

Motors

Bad bearings

Bent shaft

Wrong speeds

Optimize energy

Cracked rotor bar

Electrical defects

Pumps

Impeller Wear

Find best efficiency point

Cavitation

Leaks

Fans

Resonance and flow

Turbulence

Belt problems

Lubrication quality

Compressors

Coupling misalignment

Rotor unbalanced

Crosshead looseness

Seal wear

Gear Boxes

Tooth wear

Excessive load

Broken or cracked teeth

Bearing wear

Centrifuges

Good shoot

Bad shoot

Optimal valve operation

Out of balance

Excess load

Computer Vision - SILO

Temperatures

Humidity

Mold prevention

Fire/explosive prevention

Storage levels

Computer Vision - Energy

Temperatures

Fire/explosive prevention

Insulator faults

Wire faults

Computer Vision - Factory

Part quality

Part inspection

Package inspection

Process validation

Computer Vision - Safety/PPE

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 Embeds

FogLAMP AI integrates ML life-cycle with OT-IT:

  1. OT knowledge of the machines and processes (build and improve models continuously)
  2. The collection and labeling of data
  3. Model deployment
  4. Model runtime notifications and alerts with existing OT-IT systems
  5. 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.