FogHorn Case Studies GE Detects Early Defects and Improves Capacitor Production

	GE Detects Early Defects and Improves Capacitor Production FogHorn

GE Detects Early Defects and Improves Capacitor Production

FogHorn

Technology Category	Analytics & Modeling - Real Time Analytics
Applicable Industries	Renewable Energy
Services	Data Science Services
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. Read More
About 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. Read More
Customer Name	General Electric Company Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Applicable Industries

Renewable Energy

Services

Data Science Services

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.

About 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.

Customer Name

General Electric Company

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.

Impact #1	[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

Impact #1

[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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Overview

GE Detects Early Defects and Improves Capacitor Production

Operational Impact