Progress of data-driven process monitoring for nonlinear and non-Gaussian industry process

机译：非线性和非高斯工业流程数据驱动过程监测的进展

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According to the widely existing non-Gaussian data characteristics of nonlinear processes in complex industrial process, the developments of the existing monitoring method and its application results and shortcomings are reviewed form characteristics of non-Gaussian and nonlinear, on this basis, the present situation about data-driven process monitoring and fault diagnosis of industry process with non-Gaussian and nonlinear characteristics are analyzed. The possible direction of development and the method worthy of study and the main problem to be resolved are discussed. Finally, some problems and their research tendencies in this field are presented.

机译：根据复杂工业过程中非线性过程的广泛存在的非高斯数据特征，在此基础上审查了现有监测方法及其应用结果和缺点的发展特征，在此基础上，对现状分析了具有非高斯和非线性特性的行业过程的数据驱动过程监测和故障诊断。讨论了可能的发展方向和值得研究的方法和要解决的主要问题。最后，介绍了这一领域的一些问题及其研究趋势。

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《IEEE International Conference on Information and Automation》|2013年||共3页
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Peiliang Wang; Wuming He;
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中图分类自动化技术及设备;
关键词
data-driven process monitoring; industrial Process; non-Gaussian; nonlinear;

机译：数据驱动过程监控;工业过程;非高斯;非线性;

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2. Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis [J] . Lianfang Cai, Xuemin Tian, Sheng Chen Neural Networks and Learning Systems, IEEE Transactions on . 2017,第1期

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3. Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach [J] . Junichi Mori, Jie Yu Journal of Process Control . 2014,第1期

机译：与质量相关的非线性批处理过程性能监控，基于核的多路非高斯潜子空间投影方法
4. Progress of data-driven process monitoring for nonlinear and non-Gaussian industry process [C] . Peiliang Wang, Wuming He IEEE International Conference on Information and Automation . 2013

机译：非线性和非高斯工业过程的数据驱动过程监控进展
5. On the processing of highly nonlinear solitary waves and guided ultrasonic waves for structural health monitoring and nondestructive evaluation. [D] . Bagheri, Abdollah. 2015

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6. Linking Gaussian process regression with data-driven manifold embeddings for nonlinear data fusion [O] . Seungjoon Lee, Felix Dietrich, George E. Karniadakis, 2019

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7. Monitoring nonlinear and non-Gaussian processes using Gaussian mixture model based weighted kernel independent component analysis [O] . Cai, Lianfang, Tian, Xuemin, Chen, Sheng 2017

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8. Spectra and Covariances for 'Classical' Nonlinear Signal Processing ProblemsInvolving Class A Non-Gaussian Noise [R] . Nuttall, A. H., Middleton, D. 1991

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Progress of data-driven process monitoring for nonlinear and non-Gaussian industry process

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