Speaker "Ajay Raghavan" Details Back



Unleashing the Potential of Big Data with Hybrid AI/Physics Models for Industry 4.0


As key infrastructure assets age, maintenance budgets shrink, and goals for high performance rise year over year, system performance and optimization has become more critical than ever. PARC, a Xerox Company’s latest innovation, IIoT System Analytics, takes a hybrid approach to system performance management through a new technology suite, MOXI. By combining low-cost embedded sensors, physics-based models with cutting-edge AI and machine learning technology, MOXI is able to predict conditions and faults of interest in systems with greater than 90% accuracy, with negligible false alarm rates and near-zero missed detections. In this discussion, learn how applying guidance from PARC’s R&D team and implementing the appropriate MOXI modules, system performance can go beyond management and strive for peak optimization resulting in savings on energy, maintenance and downtime costs as well as extending overall system life and improved longer-term asset planning.
Who is this presentation for?

Prerequisite knowledge:

What you'll learn?
• Follow the evolution of system performance solutions overtime, ascertaining the limitations as well as the foundational bases on which the future of system performance and optimization is built. • Understand the theory behind hybrid physics- and machine learning-based modeling of complex systems. • Discover the six key modules essential to the success of MOXI’s IIoT System Analytics technology suite and how they complement the expertise of engineers and operations teams in the field to create truly optimized systems.


Ajay Raghavan manages the Analytics for Condition Evaluation of Systems (ACES) area within the System Sciences Lab. The ACES team is focused on developing cutting-edge analytics and sensing technologies for reliable, safe, and optimal life-cycle management of a broad spectrum of cyber-physical systems. Application areas of interest for health and condition management presently include systems in the energy, transportation, aerospace, defense, and manufacturing sectors. Ajay is the Principal Investigator on the SENSOR project under the ARPA-E AMPED program, where he is leading a multidisciplinary effort between PARC and LG Chem Power to develop fiber optic sensing-based battery management systems. Previously, he successfully led a research project on device health management. Dr. Raghavan obtained his Master's and Ph.D. work degrees in Aerospace Engineering at the University of Michigan Ann Arbor. He has a Bachelor's degree in Mechanical Engineering from the Indian Institute of Technology Bombay.