Our client is an Indian multinational steel-making company that operates across five continents and is among the largest steel manufacturers globally. The existing process for manufacturing equipment anomaly detection was primarily operator driven manual inspection with a few analytics models for a few sensors.
It took two to three months to develop a model for one equipment, and there was no standardized framework for creation and selection, making it a very analyst dependent solution. The objective was to improve equipment maintenance by building a digital twin model that is analyst independent and automatically selects the best model for equipment anomaly detection.[
The solution resulted in an immediate saving of USD 1.7 MM in terms of cost of maintenance and additional production time gained
The scalable models are being deployed in blast furnaces, crushers, fans, transformers, and similar types of equipment across the company
Transparent approach – no dependency on external IPs and solutions. On-demand future upgrades are possible due to open design concepts