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.

中文翻译：

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具有时变参数的系统的混合在线传感器错误检测和功能冗余


监视和控制系统依靠传感器发出的信号接收信息来监视系统的运行并调整操纵变量以实现控制目标。然而，传感器的性能往往受到其工作条件的限制，并且传感器还可能受到其他设备的干扰。在过程操作过程中，可能会出现许多不同类型的传感器错误，例如异常值、缺失值、漂移和噪声损坏。开发了一种混合在线传感器错误检测和功能冗余系统来检测在线信号中的错误，并用基于模型的估计替换检测到的错误或缺失值。所提出的混合系统依赖于两种技术，即异常鲁棒卡尔曼滤波器（ORKF）和局部加权偏最小二乘（LW-PLS）回归模型，它们利用了 ORKF 自动测量误差消除和数据驱动预测的优势使用 LW-PLS。该系统包括标称角度分析（NAA）方法，用于区分信号故障和由过程操作中的真实动态变化引起的传感器值的大变化。 1 型糖尿病患者的连续血糖监测 (CGM) 传感器的临床数据说明了该系统的性能。将超过 50,000 个 CGM 传感器错误添加到来自 25 个临床实验的原始 CGM 信号中，然后分析错误检测和功能冗余算法的性能。结果表明，所提出的系统可以成功地检测到大多数错误信号，并用功能冗余系统计算出的合理估计值来代替它们。