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首页> 外文期刊>International journal of system control and information processing >Monitoring of within batch and batch-to-batch dynamics using adaptive LASSO
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Monitoring of within batch and batch-to-batch dynamics using adaptive LASSO

机译：使用自适应LASSO监控批内和批间动态

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摘要

The output of a batch process under within batch operation control, and batch-to-batch feedback control can be represented by a two-dimensional auto-regressive moving average (2D ARMA) time series. However, it is not easy to identify this time series under close-loop conditions. In this paper, an adaptive least absolute shrinkage and selection operator (LASSO) method is used to identify the orders and coefficients of this 2D time series. The estimated coefficients of 2D time series model as inputs are to calculate the stability indexes by inner-matrix method. These indexes can be monitored by traditional control charts. On basis of this, the faults are detected by applying on these stability indices. The simulation results show that these stability indexes are sensitive to the faults in 2D batch processes, verifying the effectiveness of the proposed method. Furthermore, the detection capabilities are much superior when adaptive LASSO was used instead of other identification techniques.
机译：在批处理操作控制和批间反馈控制下的批处理过程的输出可以由二维自回归移动平均值（2D ARMA）时间序列表示。但是，在闭环条件下识别该时间序列并不容易。在本文中，使用自适应最小绝对收缩和选择算子（LASSO）方法来识别此二维时间序列的阶数和系数。二维时间序列模型的估计系数作为输入，将通过内部矩阵方法计算稳定性指标。这些索引可以通过传统的控制图进行监控。在此基础上，通过应用这些稳定性指标来检测故障。仿真结果表明，这些稳定性指标对二维批处理过程中的故障很敏感，证明了该方法的有效性。此外，当使用自适应LASSO代替其他识别技术时，检测能力要优越得多。

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