Semiotic Labs Case Studies ArcelorMittal condition monitoring

	ArcelorMittal condition monitoring Semiotic Labs

ArcelorMittal condition monitoring

Semiotic Labs

Technology Category	Analytics & Modeling - Big Data Analytics
Applicable Industries	Metals
Applicable Functions	Process Manufacturing
Use Cases	Machine Condition Monitoring
Challenge	ArcelorMittal’s rotating assets often operate in harsh environments. A conveyor at the company’s hot strip mill in Ghent, Belgium moves plates of sizzling hot steel along the production process. In conditions like these, traditional proximity-based technologies like vibration and acoustic analysis fail: the sensors can’t handle the high temperatures. “In the steel industry, assets frequently operate in conditions that are not hospitable to sensitive sensor technologies,” says Andy Roegis, ArcelorMittal’s industrial digitalization manager for northern Europe. “The conveyor on our hot strip mill is a critical part of the production process, but it’s virtually impossible to use manual or vibration-based techniques to assess its condition.” Read More
About Customer	ArcelorMittal is the world’s leading steel and mining company. Guided by a philosophy to produce safe, sustainable steel, it is the leading supplier of quality steel products in all major markets including automotive, construction, household applian Read More
Customer Name	ArcelorMittal Read More
Solution	ArcelorMittal is at the forefront of digitalization in the steel industry, so the company was looking for a smart solution that harnessed the power of artificial intelligence and the industrial internet of things to generate data-driven insights. “We needed a cost-effective way to improve reliability on these hard-to-monitor assets,” Roegis says. “We were looking for a realtime solution that could give us long-term insight into our conveyors’ health, performance and energy consumption.” ArcelorMittal found what it was looking for in Semiotic Labs’ SAM4. “Instead of needing to be placed on the asset in the field, SAM4 installs inside the motor control cabinet, out of harm’s way,” Roegis says. “And its advanced analytics were just what we needed.” Read More Log in to view content
Data Collected	Current, Voltage Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Applicable Industries

Metals

Applicable Functions

Process Manufacturing

Use Cases

Machine Condition Monitoring

Challenge

ArcelorMittal’s rotating assets often operate in harsh environments. A conveyor at the company’s hot strip mill in Ghent, Belgium moves plates of sizzling hot steel along the production process. In conditions like these, traditional proximity-based technologies like vibration and acoustic analysis fail: the sensors can’t handle the high temperatures.

“In the steel industry, assets frequently operate in conditions that are not hospitable to sensitive sensor technologies,” says Andy Roegis, ArcelorMittal’s industrial digitalization manager for northern Europe. “The conveyor on our hot strip mill is a critical part of the production process, but it’s virtually impossible to use manual or vibration-based techniques to assess its condition.”

About Customer

ArcelorMittal is the world’s leading steel and mining company. Guided by a philosophy to produce safe, sustainable steel, it is the leading supplier of quality steel products in all major markets including automotive, construction, household applian

Customer Name

ArcelorMittal

Solution

ArcelorMittal is at the forefront of digitalization in the steel industry, so the company was looking for a smart solution that harnessed the power of artificial intelligence and the industrial internet of things to generate data-driven insights.

“We needed a cost-effective way to improve reliability on these hard-to-monitor assets,” Roegis says. “We were looking for a realtime solution that could give us long-term insight into our conveyors’ health, performance and energy consumption.”

ArcelorMittal found what it was looking for in Semiotic Labs’ SAM4. “Instead of needing to be placed on the asset in the field, SAM4 installs inside the motor control cabinet, out of harm’s way,” Roegis says. “And its advanced analytics were just what we needed.”

Data Collected

Current, Voltage

Impact #1	[Cost Reduction - Maintenance] SAM4 eliminated unplanned downtime for the assets it monitored. During the 12-month pilot, SAM4 detected 12 developing faults in the motors driving ArcelorMittal’s hot-strip conveyors, all of which were confirmed by the mill’s maintenance engineers. “SAM4 gave us precisely the insights we needed,” says Roegis. “Up to four months in advance, in some cases. That enabled us to schedule maintenance at the optimal time.”

Impact #1

[Cost Reduction - Maintenance]

SAM4 eliminated unplanned downtime for the assets it monitored. During the 12-month pilot, SAM4 detected 12 developing faults in the motors driving ArcelorMittal’s hot-strip conveyors, all of which were confirmed by the mill’s maintenance engineers. “SAM4 gave us precisely the insights we needed,” says Roegis. “Up to four months in advance, in some cases. That enabled us to schedule maintenance at the optimal time.”

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Overview

ArcelorMittal condition monitoring

Operational Impact