A Physics- & Machine Learning-Based Approach to Models for System Performance Management & Optimization

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. Our 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.