Reconstruction-based fault identification using a combined index
Reconstruction-based fault identification using a combined index
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DOI：
10.1021/ie000141
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发表时间：
2001-10-03
影响因子：
4.2
通讯作者：
Qin, SJ
中科院分区：
文献类型：
--
作者：
Yue, HH;Qin, SJ
过程监测和故障诊断对于化工厂的高效和优化运行至关重要。提出了一种基于重构的故障识别方法，该方法采用组合索引进行多维故障重构和识别。故障检测是使用一个新的指标，结合了平方预测误差（SPE）和T-2。给出了故障可检测性的充要条件。该组合指数用于重建沿给定断层方向的断层沿着。通过假设候选故障集中的每个故障是真实故障并将重构的指数与控制限进行比较来识别故障。讨论了基于组合指标的故障可重构性和可识别性。提出了一种从历史故障数据中提取故障方向的新方法。在故障重构后的故障检测指标的基础上确定故障的维数。给出了几个仿真实例和一个实际案例。本文提出的方法与文献中两种现有的方法进行了比较，用于识别单传感器和多传感器故障。分析了其他两种方法导致错误识别结果的原因。最后，该方法被应用到一个快速热退火过程的故障诊断。从数据中提取几种典型过程故障的故障子空间，然后用于识别新的故障。
Process monitoring and fault diagnosis are crucial for efficient and optimal operation of a chemical plant. This paper proposes a reconstruction-based fault identification approach using a combined index for multidimensional fault reconstruction and identification. Fault detection is conducted using a new index that combines the squared prediction error (SPE) and T-2. Necessary and sufficient conditions for fault detectability are derived. The combined index is used to reconstruct the fault along a given fault direction. Faults are identified by assuming that each fault in a candidate fault set is the true fault and comparing the reconstructed indices with the control limits. Fault reconstructability and identifiability on the basis of the combined index are discussed. A new method to extract fault directions from historical fault data is proposed. The dimension of the fault is determined on the basis of the fault detection indices after fault reconstruction. Several simulation examples and one practical case are presented. The method proposed here is compared with two existing methods in the literature for the identification single-sensor and multiple-sensor faults. We analyze the reasons that the other two methods lead to erroneous identification results. Finally, the proposed approach is applied to a rapid thermal annealing process for fault diagnosis. Fault subspaces of several typical process faults are extracted from the data and then used to identify new faults.