Case studies in real-time fault isolation in a decentralized wastewater treatment facility

Abstract Decentralized wastewater treatment (WWT) can be an energy and resource efficient alternative to the traditional, centralized WWT paradigm for water-stressed communities. However, to operate economically, decentralized facilities do not typically have a WWT operator on-site full-time, so a real-time monitoring scheme is needed to quickly detect system faults and isolate the features associated with or affected by faults to ensure adequate treated water quality. Data collected from WWT facilities exhibit temporal dependence and experience natural fluctuations in the mean due to environmental and operator-controlled factors, violating the assumptions of many existing fault detection and isolation (FD&I) methods. To address this, we develop a complete data-driven FD&I method tuned to handle the unique features of WWT data that can be run in real-time and illustrate how it performs with data from a decentralized WWT facility in Golden, Colorado, USA. Enhanced visualization techniques are designed to assist operators in identifying features associated with the fault. We present three case studies with known faults and demonstrate how this method can aid operators in detecting and diagnosing the cause of a fault more quickly.