Quality‐relevant dynamic process monitoring based on mutual information multiblock slow feature analysis

Zheng, Haiyong; Jiang, Qingchao; Yan, Xuefeng

doi:10.1002/cem.3110

Cited by 21 publications

(7 citation statements)

References 30 publications

(61 reference statements)

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“…Equation 4is to optimize the input-output function and thus the weights make (y j ) =<ẏ 2 j >= w T j <żż T > w j is minimal. Given that x (t) is the original input data and R [15] is the covariance matrix of x (t),then…”

Section: Preliminary Knowledge a The Sfa Algorithmmentioning

confidence: 99%

“…Zheng and Yan [14] combined SFA with higher order statistics such as kurtosis to extract more effective slow features, which can identify normal and fault status. Yan and other scholars [15] proposed a method that combines SFA with mutual information-based method to extract variables more related to faults, which is superior to traditional fault detection methods. Sun et al [16] combined the deep network and SFA theory, which highlights the changed information and thus has a better detection performance.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Xiaozhi

Zhang

et al. 2020

IEEE Access

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This paper proposes a condition monitoring method based on Dynamic Related ReliefF-SFA (DRRSFA), which solves the problem of huge variable dimensions and strong autocorrelation in process monitoring. First, the samples are mapped into a new space, and the slow projection components (SPCs) of the samples are calculated. The SPCs not only considers the autocorrelation between the variables, but also characterizes the inherent properties of the whole system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, which is called the principal components(PCs) to achieve dimensionality reduction. Then the corresponding statistics and control limits are calculated based on the obtained PCs. Finally, the process monitoring using the proposed algorithm is performed by testing a numerical example and the actual production process data, and the results show the effectiveness of the proposed method. INDEX TERMS Process monitoring, slow feature analysis, slow projection components, ReliefF algorithm, projection components.

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Section: Preliminary Knowledge a The Sfa Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Xiaozhi

Zhang

et al. 2020

IEEE Access

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“…The temporal slowness can be regarded as an additional criterion in the extraction of LVs. Slow feature analysis (SFA) is a novel unsupervised LVs extraction method that can extract slowly varying LVs from temporal data and has been used in blind source separation, pattern recognition, remote sensing, and image processing. − SFA also has been concerned and favored by scholars in MSPM. − Shang et al proposed to apply SFA in process monitoring, through the analysis of experimental data, the results show that SFA can both describe the steady state and the dynamic state of the process and has improved the interpretation ability in terms of temporal coherence compared to that with classical data-driven methods . Shang et al proposed combining the fault diagnosis method based on SFA with contribution plots, which can accurately locate the fault location and find out the variations of other LVs caused by the fault .…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis

Zhou

Wen

et al. 2022

ACS Omega

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Independent component analysis (ICA) is an excellent latent variables (LVs) extraction method that can maximize the non-Gaussianity between LVs to extract statistically independent latent variables and which has been widely used in multivariate statistical process monitoring (MSPM). The underlying assumption of ICA is that the observation data are composed of linear combinations of LVs that are statistically independent. However, the assumption is invalid because the observation data are always derived from the nonlinear mixture of LVs due to the nonlinear characteristic in industrial processes. Under this circumstance, the ICA-based fault detection is unable to provide accurate detection for specific faults of industrial processes. Since the observation data come from the nonlinear mixing of LVs, this makes the observation data change faster than the intrinsic LVs on the time scale. The temporal slowness can be regarded as an additional criterion in the extraction of LVs. The slow feature analysis (SFA) derived from the temporal slowness has received extensive attention and application in MSPM in recent years. Simultaneously, the temporal slowness is expected to make up for the problem that the LVs extracted by ICA have difficulty accurately describing the characteristics of the process. To solve the above problems, this work proposes to monitor non-Gaussian and nonlinear processes using the independent slow feature analysis (ISFA) that combines statistical independence and temporal slowness in extracting the LVs. When the observation data are composed of a nonlinear mixture of LVs, the extracted LVs of ISFA can describe the characteristics of the processes better than ICA, thereby improving the accuracy of fault detection for the non-Gaussian and nonlinear processes. The superiority of the proposed method is verified by a numerical example design and the Tennessee–Eastman process.

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“…When used for modeling, the extracted slowly varying LVs will be termed as process intrinsic properties, and the fast-varying LVs are seen as process noise inversely. So far, SFA has been extensively used for various reasons, such as process monitoring, − image processing, , etc. Furthermore, ordinary SFA has been extended for diverse purposes.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Semisupervised Predictive Modeling Strategy for Industrial Continuous Catalytic Reforming Process with Incomplete Data Using Slow Feature Analysis

Jiang

Zhong

et al. 2019

Ind. Eng. Chem. Res.

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The catalytic naphtha reforming process is one of the most significant processes in the petrochemical industry. This process is notable for its function of transforming petroleum refinery naphtha from crude oil with low octane ratings into high-octane premium blending stocks, namely, gasoline or aromatic hydrocarbons. This process consists of many units along with complicated chemical reactions, which leads to large-scale and strong coupling, with time variation and nonlinearity to some extent. Under such circumstances, it remains a great challenge to control and optimize the catalytic reforming process. To evaluate the current operational status of the catalytic reforming process, there is a need for online assessment of some key quality-related indices for engineers as a reference. Among these indices, research octane number (RON) barrel is widely used for the evaluation of gasoline quality. However, traditional measurement methods are often time-consuming, labor-consuming, and expensive. To overcome such drawbacks, a data-driven predictive model for the prediction of RON barrel values is proposed in this study. Considering data from real industry are often contaminated with noise and other uncertain factors, conventional data-driven prediction methods may fail in the extraction of useful process information. Meanwhile, missing data is also commonly observed in real industrial samples. To deal with these problems, the proposed predictive model employs a semisupervised learning-based just-in-time learning framework. Different from traditional just-in-time learning frameworks, variable selection is taken into consideration in the proposed framework, in addition to sample selection. And both selection approaches proceed on the basis of the symmetric Kullback–Leibler divergence, which measures the distributional dissimilarities among samples or variables, to reduce the noise influence. Additionally, variational Bayesian principal component analysis, which is known as an effective generative model, is exploited to alleviate the missing data problem. Eventually, a novel nonlinear slow feature analysis algorithm, namely, locally weighted slow feature analysis, is put forward to model the time variance and nonlinearity of this process. To better validate the efficiency and superiority of the proposed method, an industrial case study is conducted with data collected from a real industrial catalytic reforming process, where missing data percentage ranges from 0.1 to 10%. The qualitative and quantitative results demonstrate that the proposed technique can outperform some conventional data-driven methods.

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Quality‐relevant dynamic process monitoring based on mutual information multiblock slow feature analysis

Cited by 21 publications

References 30 publications

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis

Real-Time Semisupervised Predictive Modeling Strategy for Industrial Continuous Catalytic Reforming Process with Incomplete Data Using Slow Feature Analysis

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