Semiotic Labs > Case Studies > ArcelorMittal condition monitoring

ArcelorMittal condition monitoring

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 ArcelorMittal condition monitoring - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Big Data Analytics
Applicable Industries
  • Metals
Applicable Functions
  • Process Manufacturing
Use Cases
  • Machine Condition Monitoring
The Customer
About The 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
The 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.”

The 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
Operational Impact
  • [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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