Machinery diagnostics and characterization through electrical sensing

Abstract

Two methods of non intrusive sensing and their applications for machinery condition monitoring, energy score keeping, and human activity are presented here. The first method uses existing research on Non Intrusive Load Monitoring (NILM) to refine transient detection methods using image classification techniques. Additionally building on the NilmDB framework, a new framework, TransientDB, is proposed which collects and stores information about detected transients for use in machine learning algorithms. Finally the military and civilian applications of NILM developed from multiple field tests are presented. The second method presented determines the health of machinery resilient mounts using vibration and voltage sensing, this method was developed using a multiple lab experiments, and it's utility is demonstrated in field testing on US Navy ships.