M V Karthikeyan

Project: Predictive Maintenance Scheduler (Prototype)

• Built a predictive maintenance web application within RINL's Industry 4.0 ecosystem, which houses the Kalpataru CoE, an IIoT facility with industrial IoT sensor kits, automation panels, and AI/ML platforms for equipment monitoring.
• Designed dashboards to visualize equipment usage, downtime trends, and maintenance insights, aligned with RINL's OEE and IIoT monitoring objectives.
• Gained practical exposure to how IoT sensor data pipelines feed predictive maintenance systems in a live steel plant environment.

Basic ML workflows and cloud-based model execution

• Worked on basic machine learning workflows in a cloud-based environment, performing data preprocessing and training simple models using Python.
• Managed datasets and executed Python scripts on remote cloud instances, gaining understanding of cloud infrastructure and on-demand computing resources.
• Explored how input data is processed and used by models to generate outputs in a cloud-based ML setup.