Water Utilities (WU) are responsible for supplying water for residential, commercial and industrial use guaranteeing the sanitary and quality standards established by different regulations. To assure the satisfaction of such standards a set of quality sensors that monitor continuously the Water Distribution System (WDS) are used. Unfortunately, those sensors require continuous maintenance in order to guarantee their right and reliable operation. In order to program the maintenance of those sensors taking into account the health state of the sensor, a prognosis system should be deployed. Moreover, before proceeding with the prognosis of the sensors, the data provided with those sensors should be validated using data from other sensors and models. This paper provides an advanced data analytics framework that will allow us to diagnose water quality sensor faults and to detect water quality events. Moreover, a data-driven prognosis module will be able to assess the sensitivity degradation of the chlorine sensors estimating the remaining useful life (RUL), taking into account uncertainty quantification, that allows us to program the maintenance actions based on the state of health of sensors instead on a regular basis. The fault and event detection module is based on a methodology that combines time and spatial models obtained from historical data that are integrated with a discrete-event system and are able to distinguish between a quality event or a sensor fault. The prognosis module analyses the quality sensor time series forecasting the degradation and therefore providing a predictive maintenance plan avoiding unsafe situations in the WDS.
Prognosis of Water Quality Sensors Using Advanced Data Analytics: Application to the Barcelona Drinking Water Network
