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

• 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
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.16435

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