Machine Learning Enabled Near-Real-Time Situational Response for Mechanical Systems

We propose RADEC: Reservoir-based Anomaly DEtection and Control, a system based on the reservoir computing paradigm of machine learning for model-free timeseries prediction and estimation.Reservoir computing uses fast, efficient training algorithms to learn the dynamics of a timeseries, and can be trained in an online mode to adapt to changing systems.The RADEC system consists of two main components: an anomaly detector that compares actual to predicted system responses to detect when a mechanical system has faulted, and a controller module that learns appropriate control signals to achieve desired operation even in a faulted state.