Author(s)

C. Schantz, B. Sennett, J. Donnal, M. Gillman & S. Leeb

Abstract

Better water consumption decisions benefit from detailed use information. Easily installed non-intrusive vibration sensors provide a \“no-fuss” retrofit solution for detecting the operation of water consuming appliances. The sensors measure pipe vibration, which are revealed to be a rich source of information for identifying loads. Vibration is processed to extract power spectral density based features which are classified with a clustering algorithm trained during install. The results can be used to track load operating schedule from the vibration data collected from as little as one pipe in a home. Mechanics governing the observed signals, and signal processing to extract operating information, are discussed in this paper. Field data from three different homes demonstrates the accuracy of this approach. Keywords: pipe vibration, smart water meter, consumption tracking, water utility monitoring, load classification, accelerometers, pattern matching, supervised learning, machine learning.

Keywords

pipe vibration, smart water meter, consumption tracking, water utility monitoring, load classification, accelerometers, pattern matching, supervised learning, machine learning.