TY - GEN
T1 - Machine learning based Gaussian process regression for fault detection of Biological Systems
AU - Fezai, Radhia
AU - Mansouri, Majdi
AU - Bouguila, Nasreddine
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine.
AB - Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine.
KW - Gaussian process regression (GPR)
KW - Machine learning (ML)
KW - biological process
KW - fault detection (FD)
KW - generalized likelihood ratio test (GLRT)
UR - http://www.scopus.com/inward/record.url?scp=85087527677&partnerID=8YFLogxK
U2 - 10.1109/IINTEC48298.2019.9112139
DO - 10.1109/IINTEC48298.2019.9112139
M3 - Conference contribution
AN - SCOPUS:85087527677
T3 - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
SP - 174
EP - 179
BT - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019
Y2 - 20 December 2019 through 22 December 2019
ER -