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FogHorn > Case Studies > GE Detects Early Defects and Improves Capacitor Production
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GE Detects Early Defects and Improves Capacitor Production

 GE Detects Early Defects and Improves Capacitor Production - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Real Time Analytics
Applicable Industries
  • Renewable Energy
  • Data Science Services
The Challenge

Hard to Detect Capacitor Failure Conditions Reducing Yield, Increasing Scrap

GE 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.

The Customer

General Electric Company

About The Customer

General Electric Company operates as a digital industrial company worldwide. It operates through Power, Renewable Energy, Oil & Gas, Aviation, Healthcare, Transportation, Lighting, and Capital segments. The Power segment offers technologies, solutions, and services related to energy production, including gas and steam turbines, engines, generators, and high voltage equipment; and power generation services and digital solutions. General Electric Company was founded in 1892 and is headquartered in Boston, Massachusetts.

The Solution

FogHorn Edge Intelligence Senses Defects Early in Production Cycle, Improving Yield, Reducing Scrap

To solve its multi-million-dollar scrap problems, GE asked FogHorn to apply its analytics expertise to help improve manufacturing yields. FogHorn developed a solution using its complex event processor to transform raw, streaming machine data combined with RFID into actionable parts and process quality characteristics.

Operational Impact
  • [Data Management - Real Time Data]
    • Real-time edge testing at each stage of manufacture for improved yields and reduced scrap
    • Continuous process improvement by providing real-time analytics to OT staff
    • Smart, rather than scheduled, maintenance of manufacturing equipment to maximum uptime

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