Highly Scalable ML-based Digital Twin Model for Analyzing and Forecasting Equipment Anomalies Results in USD 1.7MM Savings

Case Study

Highly Scalable ML-based Digital Twin Model for Analyzing and Forecasting Equipment Anomalies Results in USD 1.7MM Savings

Business Objective

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.

Challenges

  • Not enough data to build the model and missing data in some cases resulting in data inadequacies to build a model
  • Sensor data for certain equipment was not available due to sensors not being installed
  • Data from the installed sensors were not flowing into the database adequately

Solution Methodology 

  • Utilized sensor-based, near real-time information (Vibration, Pressure, RPM, Current, Temp. etc.) from the equipment to build a robust framework for automatic model selection for anomaly detection
  • Implemented models for both anomaly detection and forecasting (anomaly forecast for the next 72 hours) using a multi-model technique. The models analyzed huge packets of data and compared with historical downtimes to detect early warning signals
  • Initiated proactive user alerts to suggest proactive maintenance operations
  • Developed a real-time dashboard and updated it to reflect system health, anomalous behavior, 72-hr forecast of reliability, and performed a sensor-based root cause analysis

Business Impact

 

  • 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
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