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 hours 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.

中文翻译：

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多模型传感器故障检测和数据协调：糖尿病葡萄糖浓度传感器案例研究。

来自传感器的错误信息会影响过程监控。具有多种模型识别方法的算法将提高传感器故障检测和数据协调（SFD＆DR）的灵敏度和准确性。提出了一种新颖的 SFD&DR 算法，具有四种类型的模型，包括异常鲁棒卡尔曼滤波器、局部加权偏最小二乘、基于预测器的子空间识别和基于近似线性依赖的核递归最小二乘。通过人工神经网络和投票算法进一步分析残差。SFD&DR 算法的性能通过糖尿病患者人工胰腺实验的临床数据得到了证明。葡萄糖-胰岛素代谢具有时变参数和非线性，为故障检测和数据协调提供了一个具有挑战性的系统。对 896 小时内收集的 17 项临床实验的数据进行了分析；结果表明，所提出的 SFD&DR 算法能够检测和诊断传感器故障，并将错误的传感器信号与更好的模型估计值相协调。