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PLS-based fault-relevant reconstruction

机译：基于PLS的故障相关重构

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In this paper, a PLS-based fault-relevant reconstruction method is proposed for fault detection and identification. The PLS-based proposed method finds a set of latent variables to project the training data onto a process space to analyze the problem of reconstruction and is used to find the directions which can best characterize the fault effects relevant to normal status. According to the fault distribution directions, the PLS-based proposed method can eliminate the fault cause and bring faulty statistics under the control limits. The PLS-based proposed method is compared with partial least squares (PLS) to show the feasibility of the novel method, and is applied to two simulated processes. One is a simulated linear process and the other is the penicillin fermentation process. The simulated results show the improvement of the PLS-based proposed method in fault detection and identification.
机译：提出了一种基于PLS的故障相关重构方法进行故障检测与识别。基于PLS的建议方法找到一组潜在变量，以将训练数据投影到过程空间上，以分析重建问题，并用于找到最能表征与正常状态相关的故障影响的方向。根据故障分布的方向，提出的基于PLS的方法可以消除故障原因，并在控制范围内带来错误的统计信息。将基于PLS的建议方法与偏最小二乘（PLS）进行比较，以证明该方法的可行性，并将其应用于两个模拟过程。一个是模拟线性过程，另一个是青霉素发酵过程。仿真结果表明，基于PLS的方法在故障检测与识别中具有一定的改进作用。

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