scholarly journals Data‐Driven Clustering Reveals More Than 900 Small Magnitude Slow Earthquakes and Their Characteristics

2021 ◽  
Vol 48 (11) ◽  
Author(s):  
C. Aiken ◽  
K. Obara
Keyword(s):  
Data Driven ◽  
Small Magnitude ◽  
Slow Earthquakes

Data-driven fault detection methods for detecting small-magnitude faults in anaerobic digestion process

2020 ◽  
Vol 81 (8) ◽  
pp. 1740-1748 ◽  
Author(s):  
Pezhman Kazemi ◽  
Jaume Giralt ◽  
Christophe Bengoa ◽  
Jean-Philippe Steyer
Keyword(s):  
Anaerobic Digestion ◽  
Control Charts ◽  
Volatile Fatty Acids ◽  
Principal Component ◽  
Data Driven ◽  
Detection Methods ◽  
Support Vector ◽  
Statistical Control ◽  
Excellent Performance ◽  
Small Magnitude

Abstract Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.

Sign in / Sign up

Export Citation Format

Share Document