Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters. (December 2017)

• Record Type: Journal Article
• Authors: Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali
• Is Part Of: Journal of process control. Volume 60(2017)
• Page Start: 115
• Page End: 127
• Publication Date: 2017-12
• Subjects: ANN artificial neural networks -- BGC blood glucose concentration -- CGM continuous glucose monitoring -- DIP decrease followed by increase period -- DP decrease period -- EAP error appearance percentage -- EDRF error detected but reconciliation failed -- EDRS error detected and reconciled successfully -- FDD fault detection and diagnosis -- FDR false detection ratio -- GC glucose concentration -- IDP increase followed by decrease period -- IP increase period -- LW-PLS locally-weighted partial least squares -- LWR locally weighted regression -- MPR model prediction residual -- NAA nominal angle analysis -- ORKF outlier-robust Kalman filter -- PCA principal component analysis -- PLS partial least square -- PISA pressure-induced sensor attenuations -- S sensitivity -- SED&FR sensor error detection and functional redundancy -- SGF Savitzky-Golay filter -- SJCC signal jump caused by calibration -- SRR successful reconciliation rate -- SP steady period; Functional sensor redundancy -- Sensor error detection -- Kalman filter -- Locally weighted partial least squares regression -- Glucose concentrations -- CGM; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.04.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Functional sensor redundancy, sensor error detection, data reconciliation. Outlier-robust Kalman filter method. Locally weighted partial least squares regression techniques. Fault detection of glucose concentration sensor data and reconciliation. Abstract: Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50, 000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.