Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection

Miao, Aimin; Ge, Zhiqiang; Song, Zhihuan; Shen, Feifan

doi:10.1016/j.chemolab.2015.01.010

Cited by 25 publications

(14 citation statements)

References 54 publications

(106 reference statements)

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“…In many studies, the heat kernel is generally introduced to calculate the weights among the different data points. [ 25,40–44 ] Suppose that two weight matrices ( W L and W G ) take the following general form:

W_{L} ((), i, j) = ({, \begin{matrix} italicexp ((), - {‖x_{i} - x_{j}‖}^{2} / σ_{L}^{2}) & italicif x_{i} italicand x_{j} italicare italicadjacent \\ 0 & italicotherwise \end{matrix}),

W_{G} ((), i, j) = ({, \begin{matrix} italicexp ((), - {‖x_{i} - x_{j}‖}^{2} / σ_{G}^{2}) & italicif x_{i} italicand x_{j} italicare italicnonadjacent and i \neq j \\ 0 & italicotherwise \end{matrix}),

where σ L and σ G are auxiliary parameters. The adjacency graph determines the adjacent relationships between x i and x j , which can be calculated using ε neighbours and k ‐nearest neighbours.…”

Section: Improved Process Monitoring Based On Nplnlementioning

confidence: 99%

“…In Section 2.1, it is known that angle δ, which represents the trade‐off between the global and local feature extractions, is dependent on W L and W G and changes with the values of σ L and σ G . However, in traditional methods, [ 25,40–44 ] these two auxiliary parameters are only selected based on experience. Once the weak parameters are selected, the values of the two weight matrices, W L and W G , become unreasonable.…”

Section: Improved Process Monitoring Based On Nplnlementioning

confidence: 99%

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Nonparametric manifold learning approach for improved process monitoring

2021

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A novel nonparametric method based on manifold learning is proposed for industrial process monitoring. In conventional algorithms, to preserve the global and local structure information of data, heat kernels containing two auxiliary parameters are introduced to define the global and local weight matrices, respectively. However, it is difficult to identify and choose these two parameters empirically. The inadequate selection of parameters can lead to one‐sided and inappropriate global and local feature extractions, resulting in an inadequate fault detection performance. To resolve the above problems, a nonparametric strategy is used in this study to generate two nonparametric weight matrices to replace the heat kernel‐based weight matrices. Consequently, the proposed method requires no auxiliary parameters in defining the weight matrices, making it more practical. Moreover, it automatically determines a good trade‐off between global and local feature extractions. A process monitoring model based on the proposed method was developed. The feasibility and effectiveness of the new nonparametric method are evaluated using a synthetic example and the Tennessee Eastman chemical process.

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W_{L} ((), i, j) = ({, \begin{matrix} italicexp ((), - {‖x_{i} - x_{j}‖}^{2} / σ_{L}^{2}) & italicif x_{i} italicand x_{j} italicare italicadjacent \\ 0 & italicotherwise \end{matrix}),

W_{G} ((), i, j) = ({, \begin{matrix} italicexp ((), - {‖x_{i} - x_{j}‖}^{2} / σ_{G}^{2}) & italicif x_{i} italicand x_{j} italicare italicnonadjacent and i \neq j \\ 0 & italicotherwise \end{matrix}),

Section: Improved Process Monitoring Based On Nplnlementioning

confidence: 99%

Section: Improved Process Monitoring Based On Nplnlementioning

confidence: 99%

Nonparametric manifold learning approach for improved process monitoring

2021

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“…With improvements in SAR image resolution, detailed information of the image is obvious, and texture features of the building area are more abundant and applied to the information extraction of a high-resolution SAR image. Zhao, GAO, and Kuang [4] used the variation function to calculate the texture features of SAR images and applied the facial recognition and facial clustering, image indexing, and image classification [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. Bao et al presented the supervised NPE for feature extraction, using a class label to define the new distance to find the k nearest neighbors [43].…”

Section: Introductionmentioning

confidence: 99%

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Cheng

Cui

et al. 2020

IJGI

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Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.unsupervised fuzzy mean classification method to extract the building area. Texture feature images and filter images obtained by wavelet transform, according to Zhu et al. [5], are used as an input layer of the BP (Back Propagation) neural network, and the texture classification of high-resolution SAR images is completed. However, in the field of radar remote sensing, a new synthetic aperture radar (SAR) satellite constellation has been successfully launched in recent years. As SAR data have shown explosive growth, the SAR big data era has been flourishing. High-resolution polarimetric synthetic aperture radar (referred to as PolSAR) images can obtain ground scene information from multiple dimensions, on the one hand to provide rich information for identification of features and on the other hand to increase the complexity of information extraction. In the context of big data, rapid and accurate automatic extraction technology is the future development trend and is of great significance in promoting the application of PolSAR.Feature extraction is the most critical and central part of information extraction on SAR images. Studying the feature extraction method for high-resolution PolSAR images, reducing the space dimension and removing redundant information are highly important for the fast and accurate automatic extraction of building areas. Traditional feature extraction methods based on the global linear structure hypothesis premise feature of a PolSAR image set are proposed. Although they can achieve the purpose of reducing dimension, the intrinsic structure of high-dimension...

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“…For convenience, manifolds are deemed the spaces that locally look like some Euclidean space, and the calculus can be conducted on these spaces. Recently, some manifold learning algorithms, such as locality preserving projection (LPP), locally linear embedding (LLE), and local tangent space alignment (LTSA), have been applied to reveal the underlying manifold structure of the process data for complex process monitoring Xie and Shi, 2012;Miao et al, 2015). Among them, LPP is a well-known local structure analysis method that is popular due to its simple extension to new data.…”

Section: Introductionmentioning

confidence: 99%

“…To exploit global and local geometric structure information hidden in process data, combined kernel PCA with LPP to extract linear or nonlinear features for complex process monitoring. Considering that maximum variance unfolding (MVU) can reveal global structure of the data via non-local variance information and that neighborhood preserving embedding can refine the local variance information, Miao et al (2015) defined a dualobjective optimization problem that minimizes local scatter and maximizes non-local scatter simultane-ously. Zhang et al (2011) proposed a novel localglobal structure analysis approach for process monitoring in the framework of PCA and LPP.…”

Section: Introductionmentioning

confidence: 99%

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Ren

Yin

Zhao

et al. 2015

Frontiers Inf Technol Electronic Eng

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A local discriminant regularized soft k-means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

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Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection

Cited by 25 publications

References 54 publications

Nonparametric manifold learning approach for improved process monitoring

Nonparametric manifold learning approach for improved process monitoring

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

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