Robust Slow Feature Analysis for Statistical Process Monitoring,Industrial & Engineering Chemistry Research

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Robust Slow Feature Analysis for Statistical Process Monitoring
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2020-06-09 , DOI: 10.1021/acs.iecr.0c01512
Jiafeng Wang ₁ , Zhonggai Zhao ₁ , Fei Liu ₁

Affiliation

Slow feature analysis (SFA) is being adopted in the process monitoring and fault diagnosis as a new latent variable extraction and dimension reduction method. As temporally relevant dynamic features extracted by SFA, slow features (SFs) can reveal typical systematic trends. However, SFA cannot resist the influence of outliers, which can deteriorate the performance of the SFA monitoring model since SFA considers that the modeling data contain only slow features and quickly varying noise. In this study, a robust SFA (RSFA) method based on the M-estimator is proposed, based on which a robust SFA monitoring model is established. Such a method can eliminate the steady and dynamic anomalies due to outliers while obtaining a precise estimation of normalization factors. It properly detects outliers in the eigendecomposition and replaces them with suitable values. Finally, the feasibility and effectiveness of the RSFA monitoring method are demonstrated by a numerical simulation and Tennessee Eastman (TE) benchmark process.

中文翻译：

用于统计过程监控的鲁棒慢特征分析

慢特征分析（SFA）作为新的潜在变量提取和降维方法正在过程监控和故障诊断中采用。随着SFA提取与时间相关的动态特征，慢速特征（SF）可以揭示典型的系统趋势。但是，SFA无法抵御离群值的影响，这可能会使SFA监视模型的性能下降，因为SFA认为建模数据仅包含慢速特征和快速变化的噪声。在这项研究中，提出了一种基于M估计器的鲁棒SFA（RSFA）方法，在此基础上建立了鲁棒SFA监视模型。这种方法可以消除异常值引起的稳态和动态异常，同时获得对归一化因子的精确估计。它可以正确地检测特征分解中的异常值，并用适当的值替换它们。最后，通过数值模拟和田纳西州伊斯曼（TE）基准测试过程证明了RSFA监测方法的可行性和有效性。

更新日期：2020-07-08

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