Dr. Gopinath Rajendiran

Lead digital transformation initiatives by integrating IoT data, diagnostics, and KPI development into operations to enable predictive maintenance and advanced analytics that improve reliability and efficiency. Designed and implemented an end-to-end on-premise industrial data pipeline for real-time applications. Worked with global data science teams and product managers (Germany & UK) to design and deliver scalable digital solutions across the process industry.

Leveraged IoT sensor data, statistical analysis, and unsupervised learning for real-time EC fan monitoring, and developed a vibration score based algorithm for condition monitoring, enabling anomaly detection, proactive maintenance, improved reliability, and extended equipment lifespan. Led cross-functional teams and partnered with global data science & product innovation experts (Germany and US) to drive digitalization and scalable product development.

Developed and deployed edge-based algorithms for condition monitoring of electrical motors (current and vibration signals), and built AI-driven models for anomaly detection in smart energy meters, event detection, and energy demand forecasting, improving operational efficiency and energy management. Led cross-functional teams across hardware, firmware, and UI/UX to deliver integrated digital solutions.

• Worked on improving the performance of non-intrusive load monitoring (NILM) systems for monitoring the electrical utilities/loads in built environments for smart sustainable cities. • Fine-tuning and implementation of the machine learning/deep learning algorithms in hardware development boards for real time deployment of NILM device. • Implemented and studied the performance of the AI based NILM system in the field trails.

• Worked on monitoring the electrical utilities/loads using non-intrusive load monitoring (NILM) technique. • Developed machine learning algorithms to monitor the events and energy consumption of electrical loads. • Implemented machine learning algorithms in Hardware development board and tested the system in real-time scenarios.

Developed fault diagnostic system for predictive maintenance of electrical machines using machine learning algorithms.

I have worked on electromagnetic performance analysis of metamaterial based frequency selective surfaces for aerospace applications.

Condition monitoring of electrical machines using AI/ML algorithms