Manufacturing / Steel
POSCO: Anomaly alerts for steelmaking equipment
- Company
- POSCO
- Country
- South Korea
- Adoption stage
- Limited operation
- Source published
- Date basis
- The date the source was published. It can differ from the date adoption started.
- How the source was checked
- Read the full source text
The work problem
Because of the high heat and the complex equipment layout at a steelworks, there were subtle signs of trouble that are hard to confirm by eye. Inside a reheating furnace the temperature is very high, so it was hard for operators to go close and see whether a valve was working properly. A small oil leak from a tank was also hard for an operator to notice straight away.
Technology and data
Field engineers in the EIC technology department at Pohang Works developed the predictive maintenance logic themselves. For the tuyeres on the No. 3 blast furnace body, they built logic that analyses electromagnetic flow meter and temperature data in real time and raises an alarm immediately if abnormal values persist for a set period. In the reheating furnace, the opening of a valve is compared with the amount of gas actually flowing and shown as a graph. If the slope of the graph differs from usual, they immediately recognise whether a part inside the valve has failed or air is short, and repair it. Oil tanks are monitored automatically around the clock with shift register technology. If the oil volume falls abnormally against the previous day, the system detects it at once and an engineer goes out for a field check.
Results
Pohang Works said that in May of last year, as of the announcement, it found potential failure causes early through two sensor alarms alone and carried out maintenance. In reheating furnace valve monitoring it was credited with achieving both safety and work efficiency. The EIC technology department staff who led the development said the core was turning complex field data into graphs and an alarm scheme anyone can understand. The company said it plans to keep extending the smart maintenance logic it developed in house to other equipment and spread a preventive maintenance culture.
Limits and open questions
The company presents this as AI based, but the logic described in the source is explained as persistence of abnormal values, comparison of gas volume against valve opening, and detection of a fall against the previous day. What role a trained model played is not confirmed.
Sources
- 보도자료 - PRESS - 포스코 미디어센터posco.com, Accessed
Compiled from public sources. These are not results from ATF Works customers.