Keywords
data quality assessment; fault detection; on-line instrumentation
Location
Session G2: Data Mining for Environmental Sciences (s-DMTES IV)
Start Date
17-6-2014 2:00 PM
End Date
17-6-2014 3:20 PM
Abstract
Compared to traditional grab sampling modern measurement systems enable continuous water quality monitoring of water systems at high frequency. However, in real world applications on-line sensors are still subject to functional, technical and operational constraints. Challenges thus remain associated with the automation of data collection and especially data validation to ensure proper use and interpretation of the data and avoid the danger of building data graveyards. Poor quality data could drastically affect the results of their application, e.g. water quality models for river basin management, model-based control, WWTP control rules, decision making, etc. For practical fault detection purposes, in this paper, a data-driven tool that attempts to extract useful information from the time series of multiple measurement signals, in the absence of exact process knowledge, is presented. The proposed tools are successfully tested on on-line water quality time series from different applications including sewers, wastewater treatment plants and receiving waters.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Automated data quality assessment: Dealing with faulty on-line water quality sensors
Session G2: Data Mining for Environmental Sciences (s-DMTES IV)
Compared to traditional grab sampling modern measurement systems enable continuous water quality monitoring of water systems at high frequency. However, in real world applications on-line sensors are still subject to functional, technical and operational constraints. Challenges thus remain associated with the automation of data collection and especially data validation to ensure proper use and interpretation of the data and avoid the danger of building data graveyards. Poor quality data could drastically affect the results of their application, e.g. water quality models for river basin management, model-based control, WWTP control rules, decision making, etc. For practical fault detection purposes, in this paper, a data-driven tool that attempts to extract useful information from the time series of multiple measurement signals, in the absence of exact process knowledge, is presented. The proposed tools are successfully tested on on-line water quality time series from different applications including sewers, wastewater treatment plants and receiving waters.
