Automated Predicitive Analytics For Steel/Metals Industry
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
- Automation & Control - Programmable Logic Controllers (PLC)
- Functional Applications - Enterprise Asset Management Systems (EAM)
- Sensors - Pressure Sensors
Applicable Industries
- Metals
Applicable Functions
- Maintenance
Use Cases
- Predictive Maintenance
About The Customer
A Steel Manufacturing Plant Produces Ponstruction Grade Rebar. 24 Hours a Day, 7 Days a Week, Scrap Metal is Converted into Steel. Each Year the Plant Produces 1.1 Billion Pounds of Steel Rebar Used in Concrete. That’s Enough Rebar to Circle the Glo
The Challenge
Asset to be monitored: Wire Compactor that produces Steel Rebar
Customer Faced The Following Challenges:
- Dependent upon machine uptime.
- Pressure cylinders within the compactor fail to control compression and speed causing problems in binding the coil.
- Equipment failure occurs in the final stage of production causing the entire line to stop, can you say bottleneck?
- Critical asset unequipped with sensors to produce data.
The Solution
The Results:
- SORBA-SDC (Smart Data Collector) connected to a Siemens PLC, collecting months of time series data to analyze behavior of equipment.
- Eliminated unplanned downtime for critical plant assets.
- Improved asset utilization rates, thus optimizing or reducing CapEx/OpEx costs.
- Minimized cost but not engaging IT personnel to connect into the enterprise network.
- SORBA does not require any firewall ports to open.
- Detected anomalies with lead times up to 20 days. Correlated all potential issues with work order events.
- Achieved an operational savings, in one application, of over $100,000 annually at just one site.
Data Collected
Downtime, Equipment Status, Fault Detection, Machine Performance, Process Procedure
Operational Impact
Quantitative Benefit
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