Identifying household water use through transient signal classification

Abstract

The research reported in this paper aims to develop a household water use identification method through signal pattern analysis. An experimental facility was constructed to simulate bathroom and kitchen water use. The data acquisition system used a volumetric water meter with pulsed output, pressure transducers, data acquisition with a Universal Serial Bus interface interconnected with the Cyble sensor and a laptop computer. The data analysis was performed using a pattern recognition algorithm to identify the hydraulic fixtures in use. Five classes of water use were considered, as follows: (1) kitchen faucet (KF), (2) washbasin faucet (WF), (3) bidet (BD), (4) shower (SH), and (5) toilet flush (TF). Two algorithms were used to identify the best classifier for the data, as follows: (1) multilayer perceptron, and (2) support vector machine (SVM). The fusion by majority vote regarding the results of SVM in the time domain showed the best accuracy; 92% accuracy for kitchen faucet, 94% for washbasin faucet, 94% for bidet, 100% for the shower, and 100% for toilet flush, thus supporting the use of signal signatures of flow and pressure in identifying the hydraulic fixtures in use.