Modified LPP based on Riemannian metric for feature extraction and fault detection

Shah, Muhammad Zohaib Hassan; Hu, Li-Sheng; Ahmed, Zahoor

doi:10.1016/j.measurement.2022.110923

Cited by 17 publications

(4 citation statements)

References 25 publications

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“…The Riemannian metric learning has been widely acknowledged as an effective methodology to learn the discriminative Riemannian metric, which encodes the intrinsic geometric structure of the Riemannian manifold. , The Riemannian metric allows calculating the distance and capturing the relationship between data in a manifold. Shah et al developed a process monitoring approach where manifold’s geometric information within process data is revealed by the Riemannian metric. Yang et al .…”

Section: Introductionmentioning

confidence: 99%

Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

Fu,

Luo,

2024

Ind. Eng. Chem. Res.

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Due to industrial processes usually characterized by multiple operating units and complex interactions, process data is formulated with hybrid structures, indicating that Euclidean and non-Euclidean structures simultaneously exist among data, which poses a great challenge for process monitoring. However, existing monitoring methods tend to analyze data in terms of a single subject in which a single Euclidean metric or non-Euclidean metric is employed to represent data, which probably deteriorates the model accuracy. This paper proposes a novel method called Euclidean-Riemannian joint low-rank projections for process monitoring. A multiple structure embedding-guided learning framework, in which Euclidean space and Riemannian manifold are mapped into a common subspace, is developed to exploit the underlying information on heterogeneous data spaces. Furthermore, the low-rank constraint is exploited to alleviate the negative influence of corruption so that monitoring results become more reliable. The l 2,1 norm is forced on projection matrix, which enables the proposed approach to be more flexible in the selection of useful information. By this means, the reduced-dimensional representations captured can give more insights into the intrinsic information on data, enhancing the fault detection capability. The experimental results on the Tennessee benchmark platform and the real-world industrial processes, namely fluidized catalytic cracking process, show the effectiveness of our proposed approach.

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Section: Introductionmentioning

confidence: 99%

Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

Fu,

Luo,

2024

Ind. Eng. Chem. Res.

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“…However, these algorithms maintain only the data's global structure while ignoring the local features of the data. Stream-learning methods represented by LPP [17][18][19] and neighborhood preserving embedding (NPE) [20][21][22] have been developed and widely used in recent years. These methods can extract hidden intrinsic properties from high-dimensional data and maintain their local structural features.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection Algorithm Based on Dynamic Global–Local Preserving Projection

Wang,

Zhang,

Zheng

2023

Applied Sciences

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Industrial system operations usually have dynamic characteristics. If these characteristics are ignored, the performance of fault detection degrades. Herein, the fault-detection algorithm of dynamic global–local preserving projection (DGLPP) is employed to solve the problem mentioned. First, time-delay data are added to the sample to form an augmentation matrix and characterize the system dynamics. Second, the dimensionality of the augmented matrix is reduced using global–local preserving projection. The dimensionality-reduction method can preserve the data’s global and local structures. Then, a DGLPP model is built using the dimensionality-reduced data. Moreover, Hotelling’s T2 and squared prediction error (SPE) statistics are used for fault detection. Finally, this method is used to detect the fault in the Tennessee Eastman (TE) process. The experimental results show that the DGLPP method has an enhanced fault detection rate. Moreover, the fault-detection effects of the DGLPP method are better than those of the principal component analysis (PCA), local preserving projection (LPP), and global–local preserving projection (GLPP) methods.

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“…Yu (2012) proposed local and global PCA (LGPCA) by considering both the local and global information. Following this novel approach, several new methods have been formulated (He and Xu, 2016;Luo et al, 2016;Tong and Yan, 2014;Zhan et al, 2019) Despite all the advances mentioned above, there are still some gaps in these methods (Orzechowski et al, 2020;Perraul-Joncas and Meila, 2017;Shah et al, 2022). The most important criticism is that manifold learning methods fail to reveal underlying geometric structure in many cases (Perraul-Joncas and Meila, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Despite all the advances mentioned above, there are still some gaps in these methods (Orzechowski et al, 2020; Perraul-Joncas and Meila, 2017; Shah et al, 2022). The most important criticism is that manifold learning methods fail to reveal underlying geometric structure in many cases (Perraul-Joncas and Meila, 2017).…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

Shah

Ahmed

2022

Transactions of the Institute of Measurement and Control

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Manifold learning is widely adopted for the fault detection of industrial processes. However, the quality of low-dimensional embedding coordinates can be adversely affected by ill-constructed graph Laplacian. An improved locality preserving projection (ILPP) scheme is proposed. ILPP is built on a geometrically inspired Laplacian, and the Riemannian metric is used to find the suitable bandwidth parameter. The proposed approach combines the advantages of ILPP in preserving manifold data structures and those of support vector data description (SVDD) in handling complex process data distributions. Case studies on helix data, hot strip mill, and Pensim benchmark processes demonstrate the utility and feasibility of the proposed approach. The average fault detection rate for proposed ILPP is 99%, which is higher than locality preserving projection (LPP; 87.8%), local tangent space alignment (LTSA; 74.9%), and principal component analysis (PCA; 90.6%).

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Modified LPP based on Riemannian metric for feature extraction and fault detection

Cited by 17 publications

References 25 publications

Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

Fault Detection Algorithm Based on Dynamic Global–Local Preserving Projection

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

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