Abstract
Today, the development and testing of methods for fault detection and identification in wastewater treatment research relies on two important assumptions: (i) that sensor faults appear at distinct times in different sensors and (ii) that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit fault symptoms that appear simultaneously and with similar intensity. Consequently, this suggests that future research should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the first empirically validated sensor fault model for wastewater treatment simulation, which is useful for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for remote sensor validation in decentralized wastewater treatment systems.
Highlights
By several accounts, the lack of online sensor data quality poses a long-standing challenge (Rieger et al, ; Rosén et al )
Most techniques implicitly require that sensor fault symptoms appear independently from each other; that is, the probability that two sensor faults start at the same time is assumed to be zero
The results reveal that commonly held assumptions regarding sensor faults and fault symptoms are false
Summary
The lack of online sensor data quality poses a long-standing challenge (Rieger et al , , ; Rosén et al ). Data-analytical techniques can enable automated and remote detection of sensor faults. The fourth category relies on spatial redundancy, relating signals produced at distinct locations or for different variables. Examples of this category include methods based on first principles; for example, balance equations, and methods rooted in statistical practice; for example, principal component analysis. Each of these advanced methods require tuning to find an optimal trade-off between correct alarms and false alarms. Most techniques implicitly require that sensor fault symptoms appear independently from each other; that is, the probability that two sensor faults start at the same time is assumed to be zero
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