Locality preserving based data regression and its application for soft sensor modelling

Miao, Aimin; Li, Peng; Ye, Lingjian

doi:10.1002/cjce.22568

Cited by 7 publications

(6 citation statements)

References 49 publications

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“…[12] In our study, we set n as the minimum number of sequentially-ordered feature values whose cumulative value is greater than 99.99% of the sum for all feature values. The kernel width parameter B of the kernel function can be calculated according to Equations ( 23) and (24), where B ¼ 80 m in the KPCA, DKPCA, and LDKPCA models. Regarding the selection of principal component, the size of CPV is calculated according to Equation (19), and the number of principal components with a CPV larger than 90% of the overall eigenvalues is selected.…”

Section: Fault Detection Resultsmentioning

confidence: 99%

“…However, the local structure information in industrial process data is extremely valuable for feature extraction and data mining. In recent years, dimensionality reduction methods based on manifold learning have been rapidly developed, and a large number of nonlinear algorithms, such as Laplacian eigenmaps (LE), [ 21 ] locally linear embedding (LLE), [ 22 ] isometric feature mapping (ISOMAP), [ 23 ] and local preserving projections (LPP), [ 24,25 ] have been proposed and proved to be powerful in process monitoring capability for detection and diagnosis. Among them, Jiang et al [ 26 ] proposed a semi‐supervised machine fault monitoring method based on LE, and Huang et al [ 27 ] applied the LE method to the fault diagnosis of a rolling bearing.…”

Section: Introductionmentioning

confidence: 99%

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Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

2023

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Dynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as the T2 and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.

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Section: Fault Detection Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

2023

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“…The complete list of variables is given in Table 1. 9,50 The TE process has one normal operating condition and 21 faulty operating conditions. 51 The information about these faults is summarized in Table 2.…”

Section: Te Processmentioning

confidence: 99%

Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

Tan

Miao

et al. 2019

Measurement and Control

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This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.

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“…Therefore, a large number of scholars have carried out a series of studies and applications on data-driven modelling methods. Among them, the most widely used linear method is a multiple statistical regression, such as Gaussian process regression (GPR) and partial least squares regression [17][18][19]. Because these methods are simple, they have strong practicability, but they are also prone to errors when dealing with complex data, especially data with impurities.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

Wang

Fan

Pan

et al. 2020

Sensors

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The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R2 values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

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Locality preserving based data regression and its application for soft sensor modelling

Cited by 7 publications

References 49 publications

Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

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