Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes

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...

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Bibliographic Details
Published in:AIChE journal 2019-02, Vol.65 (2), p.629-639
Main Authors: 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
Format: Article
Language:English
Subjects:
Algorithms
artificial neural network
Artificial neural networks
Case studies
data reconciliation
Dependence
Diabetes
Diabetes mellitus
Fault detection
Fault diagnosis
Glucose
Glucose metabolism
Identification methods
Information processing
Insulin
Kalman filter
Kalman filters
kernel filter
Least squares
Metabolism
Neural networks
Organic chemistry
Outliers (statistics)
Pancreas
partial least squares
Sensors
subspace identification
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Summary: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
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16435