{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/100144"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/100144","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"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. 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