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FogHorn
Edge Intelligence for Industrial and Commercial IoT
Overview
HQ Location
United States
Year Founded
2014
Company Type
Private
Revenue
$10-100m
Employees
51 - 200
Website
Twitter Handle
Company Description
FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT applications. FogHorn’s software platform brings the power of Machine Learning and advanced analytics to the On-Premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, asset performance optimization, operational intelligence and Predictive Maintenance use cases.
IoT Snapshot
FogHorn is a provider of Industrial IoT analytics and modeling, and infrastructure as a service (iaas) technologies, and also active in the automotive, buildings, cities and municipalities, healthcare and hospitals, mining, oil and gas, renewable energy, retail, transportation, and utilities industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
FogHorn’s Technology Stack maps FogHorn’s participation in the analytics and modeling, and infrastructure as a service (iaas) IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
Case Studies.
Case Study
Pump Cavitation Detection
Cavitation is a condition can occur in centrifugal pumps when there is a sudden reduction in fluid pressure. Pressure reduction lowers the boiling point of liquids, resulting in the production of vapor bubbles if boiling occurs. This is more likely to happen at the inlet of the pump where pressure is typically lowest. As the vapor bubbles move towards the outlet of the pump where pressure is higher, they rapidly collapse (return to a liquid state) resulting in shock waves that can damage pump components.
Case Study
GE Detects Early Defects and Improves Capacitor Production
Hard to Detect Capacitor Failure Conditions Reducing Yield, Increasing ScrapGE was facing multi-million-dollar scrap problems due to limited real-time insights into the entire production process. They believed they could significantly improve the yield and reduce the scrap of their manufacturing operation by analyzing a large amount of RFID sensor data being produced by 30+ machines during the production cycle. This included correlating processing data in real-time from several sources to create an edge intelligence layer with FogHorn for real-time condition monitoring throughout the production process. The goal was to identify defects early, quickly determine the root cause, and speed remediation actions to improve yield and reduce scrap costs.
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