Device - Macbook Air

Challenge / Problem Statement

Hindalco Industries faced multiple challenges in monitoring and maintaining its Anode Baking Furnace (ABF):

  • Manual inspections were risky, time-consuming, and prone to oversight.
  • Traditional surveillance systems failed in harsh industrial environments.
  • Lack of real-time monitoring delayed defect detection and maintenance.
  • High unplanned downtime costs due to undetected defects.
  • Limited visibility into furnace health and maintenance cycles.

Objectives

  • Automate defect and anomaly detection across ABF operations.
  • Improve worker safety by reducing manual inspection risks.
  • Provide real-time visibility into furnace conditions.
  • Enable predictive maintenance with AI-driven alerts.
  • Build a scalable industrial surveillance platform adaptable across plants.

Process & Implementation

  • Developed ML models to detect and classify defects from furnace visuals.
  • Applied OpenCV-based vision pipelines for anomaly recognition.
  • Designed and deployed a Wi-Fi controlled rover with endoscopic cameras for in-depth furnace pit inspections.
  • Built a centralized monitoring portal with dashboards for defects, reports, and maintenance cycle tracking.
  • Integrated notification services for critical alerts and anomaly detection.
  • Deployed industrial-grade cameras and equipment to withstand harsh conditions.

Our Solution

We engineered an end-to-end AI-powered surveillance system for Hindalco:

  • Automated anomaly and defect detection using AI + OpenCV.
  • A custom-built rover equipped with cameras for internal furnace pit inspections.
  • Real-time monitoring portal with defect tracking, analytics, and historical logs.
  • Notification and alerting system for immediate incident response.
  • Scalable and robust design, adaptable for multiple industrial environments.

Tools & Tech Used

  • Platform: AVEVA PI System
  • AI/ML: Digital Twin, Predictive & Prescriptive Models, GenAI
  • Data Infrastructure: Enterprise Data Lake, Real-Time Visualization
  • Culture Enablement: Digital Council, Training & Change Management Programs

Results & Impact

  • Achieved 100% furnace coverage through AI-powered surveillance.
  • 70% reduction in manual inspection time and effort.
  • Significantly improved worker safety by minimizing hazardous on-site inspections.
  • Enabled predictive maintenance that reduced unplanned downtime and costs.
  • Delivered a scalable platform ready for deployment across multiple manufacturing plants.

Key Takeaways

  • AI-driven surveillance combined with robotics ensures safer and smarter plant monitoring.
  • Real-time anomaly detection leads to faster decision-making and reduced risks.
  • Integration of OpenCV, machine learning, and IoT hardware unlocks true Industry 4.0 potential.
  • MoreYeahs builds scalable AI-powered industrial solutions that transform manufacturing efficiency.

Project Duration & Team

  • Duration: 6-8 weeks
  • Team:1 Robotics Engineer, 1 AI/ML Engineer, 1 Backend Developer, 1 QA Analyst
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