Grey bootstrap method for data validation and dynamic uncertainty estimation of self-validating multifunctional sensors
Grey bootstrap method for data validation and dynamic uncertainty estimation of self-validating multifunctional sensors
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DOI：
10.1016/j.chemolab.2015.05.003
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发表时间：
2015-08-15
影响因子：
3.9
通讯作者：
Wang, Qi
中科院分区：
文献类型：
--
作者：
Chen, Yinsheng;Jiang, Shouda;Wang, Qi
多功能传感器输出的准确性和可靠性直接影响着化工过程测控系统的运行状态和性能。鉴于其重要性，自我验证的多功能传感器，以提高在操作中的测量的可靠性。提出了一种基于灰色自助法（GBM）的自验证多功能传感器在线数据验证和动态不确定度估计新策略。将基于GBM的数据验证算法和工作原理应用于多故障检测、隔离和恢复（FRENT）。该方案能同时隔离多个传感器的多故障，并能实现故障恢复，具有较高的准确性和较好的实时性。此外，它具有良好的性能，区分无故障信号的突变和排除故障。传统的不确定度表示方法在动态测量中由于不确定度的概率分布未知和样本量小而存在局限性。作为一种数据驱动的方法，GBM可以在没有测量值概率分布先验信息的情况下，实时地从贫信息中评估测量不确定度。通过计算机仿真和真实的化学气体浓度监测实验系统验证了该策略的性能。通过对不同方法的比较，结果表明GEM在自确认多功能传感器的数据确认和动态不确定度估计中具有优越性。(C)2015 Elsevier B.V.版权所有。
The accuracy and reliability of multifunctional sensor outputs directly influence the running state and performance of measurement and control systems in chemical processes. Given their importance, self-validating multifunctional sensors are presented to improve the reliability of measurements in operation. A novel strategy based on the grey bootstrap method (GBM) is proposed for the online data validation and dynamic uncertainty estimation of self-validating multifunctional sensors. The data validation algorithm and the working principle based on GBM are applied for multiple faults detection, isolation and recovery (FDIR). The proposed FDIR scheme can simultaneously isolate multiple faults of multifunctional sensors and accomplish failure recovery with high accuracy and good timeliness. Moreover, it has a good performance of discriminating between fault-free signals with sudden changes and undoubted faults. On account of the unknown probability distribution and small sample size, the traditional expression of uncertainty has limitation in dynamic measurements. As a data-driven method, the GBM can evaluate the measurement uncertainty from poor information without prior information about the probability distribution of measur and in real-time. The performance of the proposed strategy is verified by computer simulations and a real experimental system of chemical gas concentration monitoring. Through the comparison of different methods, the results show that the GEM has superiority for the data validation and dynamic uncertainty estimation of self-validating multifunctional sensors. (C) 2015 Elsevier B.V. All rights reserved.