Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters

摘要

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.AbstractSupervision 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 mod