Isolating incipient sensor fault based on recursive transformed component statistical analysis
Isolating incipient sensor fault based on recursive transformed component statistical analysis
复制标题
基于递归变换分量统计分析隔离早期传感器故障
DOI：
10.1016/j.jprocont.2018.01.002
复制
发表时间：
2018-04-01
影响因子：
4.2
通讯作者：
Zhou, Donghua
中科院分区：
文献类型：
--
作者：
Shang, Jun;Chen, Maoyin;Zhou, Donghua
本文研究了传感器早期故障的隔离问题。基于递归变换分量统计分析（RTCSA），提出了两种不同的隔离方法。第一种方法称为子空间重构，即去除特定子空间中的元素，然后通过最小化重构的检测指标进行重构。故障变量由最小尺度重构检测指标确定。第二种方法称为子块检测，其在线计算复杂度较低。在每个滑动窗口中依次选择测量矩阵的子块，计算子块检测指标，根据最大的子块检测余量确定故障变量。通过数值算例和连续搅拌槽式反应器的仿真，与现有的基于重构贡献图（RBC）及其变体平均残差重构贡献图（ARdR-CP）等隔离方法进行了比较，证明了所提方法的优越隔离性能。(C) 2018 Elsevier Ltd.版权所有。
This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor. (C) 2018 Elsevier Ltd. All rights reserved.