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Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes
Author(s) -
Feng Jianyuan,
Hajizadeh Iman,
Yu Xia,
Rashid Mudassir,
Samadi Sediqeh,
Sevil Mert,
Hobbs Nicole,
Brandt Rachel,
Lazaro Caterina,
Maloney Zacharie,
Littlejohn Elizabeth,
Quinn Laurie,
Cinar Ali
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16435
Subject(s) - continuous glucose monitoring , fault detection and isolation , fault (geology) , diabetes mellitus , computer science , chemistry , real time computing , type 1 diabetes , medicine , endocrinology , artificial intelligence , biology , actuator , paleontology
Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor‐based subspace identification, and approximate linear dependency‐based kernel recursive least squares is proposed. The residuals are further analyzed by artificial neural networks and a voting algorithm. The performance of the SFD&DR algorithm is illustrated by clinical data from artificial pancreas experiments with people with diabetes. The glucose‐insulin metabolism has time‐varying parameters and nonlinearities, providing a challenging system for fault detection and data reconciliation. Data from 17 clinical experiments collected over 896 h were analyzed; the results indicate that the proposed SFD&DR algorithm is capable of detecting and diagnosing sensor faults and reconciling the erroneous sensor signals with better model‐estimated values. © 2018 American Institute of Chemical Engineers AIChE J , 65: 629–639, 2019