Abstract Considering dynamic and static characteristics in industrial processes, this paper proposed an integrated monitoring approach based on slow feature analysis and independent component analysis (SFA-ICA), which can fully take advantage of SFA and ICA in extracting dynamic features and static non-Gaussian features. A sequential correlation-based matrix for each variable is first calculated to evaluate the dynamics of the process variable, in which, the variables with weak autocorrelation and cross-correlation are considered as static variables, while the others are dynamic variables. Then, the ICA and SFA algorithms are built for the static and dynamic subspaces. The statistics from each of the subspaces are combined using Bayesian inference to give a final comprehensive statistic. The proposed SFA-ICA monitoring approach is applied to a numerical example, the Tennessee Eastman (TE) process and the continuous stirred tank reactor (CSTR) process. Results show that the SFA-ICA achieves the better fault detection rates for the numerical example, the CSTR process, and several typical faults for TE process.

中文翻译：

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基于慢特征分析独立组件分析的工业过程综合监控方法结合动静态特征

摘要 针对工业过程中的动静态特性，提出一种基于慢特征分析和独立分量分析的综合监控方法（SFA-ICA），充分利用SFA和ICA在动态特征提取和静态非高斯特征分析中的优势。特征。首先计算每个变量的基于序列相关的矩阵来评估过程变量的动态，其中自相关和互相关弱的变量被认为是静态变量，而其他变量是动态变量。然后，为静态和动态子空间构建了 ICA 和 SFA 算法。来自每个子空间的统计数据使用贝叶斯推理进行组合，以给出最终的综合统计数据。建议的 SFA-ICA 监测方法应用于数值示例，田纳西伊士曼 (TE) 工艺和连续搅拌釜反应器 (CSTR) 工艺。结果表明，SFA-ICA 对数值示例、CSTR 过程和 TE 过程的几个典型故障实现了更好的故障检测率。