INDUSTRIAL AI

The Industrial AI cluster develops and deploys data-driven intelligence that improves manufacturing productivity, quality, and resilience across the full operational lifecycle. We integrate machine learning, statistical modeling, and physics-informed approaches with plant-floor data (sensors, PLC/SCADA, MES/ERP, vision systems) to enable real-time decision support and closed-loop optimization. Core strengths include predictive analytics, anomaly detection, machine vision inspection, and multivariate process control for high-mix, high-variability environments. Emphasis is placed on measurable impact—reduced downtime, improved yield, faster root-cause diagnosis, and more stable production.

A distinguishing focus is translating AI from lab prototypes into robust shop-floor systems through MLOps, edge AI, and trustworthy AI practices. The cluster builds repeatable pipelines for data governance, model monitoring/drift detection, and safe deployment with human-in-the-loop workflows. We also advance explainable and risk-aware AI to support operator trust, compliance, and auditability in safety- and quality-critical operations. These capabilities position the cluster to partner with industry on scalable, secure, and maintainable Industrial AI solutions.

Representative Research Topics

  • Predictive maintenance and remaining useful life (RUL) modeling for critical manufacturing assets

  • Machine vision quality inspection: defect detection/segmentation, metrology, and inline QA automation

  • Anomaly detection and fault diagnosis using multimodal time-series (vibration, acoustics, current, thermal, etc.)

  • Process optimization and control with hybrid models (physics-informed ML, digital-twin-assisted optimization)

  • Edge AI + MLOps for manufacturing: deployment, monitoring, drift management, and trustworthy/explainable AI

Group Members

  • Nelson Granda, Assistant Professor, Applied Engineering Technology - nagranda@ncat.edu

  • Letu Qingge, Assistant Professor, Computer Science - lqingge@ncat.edu
  • Renzun Zhao, Associate Professor, Civil, Architectural and Environmental Engineering - rzhao@ncat.edu