A Novel Dynamic Process Monitoring Algorithm: Dynamic Orthonormal Subspace Analysis

Hao, Weichen; Lu, Shan; Lou, Zhijiang; Wang, Yonghui; Jin, Xin; Syamsunur, Deprizon

doi:10.3390/pr11071935

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…For instance, the PCA-based control charts and associated modifications are all committed to transforming the raw data into a reduced set of independent features, thus employing Hoteling's T 2 statistic, under the presumed gaussianity. After that, other machine learning methods, such as CCA and PLS [14][15][16] as well as some deep unsupervised architectures [17][18][19][20][21], also value this idea and are devoted to the construction of the same statistic from the reduced space of transformed features. It was until the inception of the variational auto-encoder (VAE) that deep learners switched focus on using strong transformation to secure the distribution premise before using them for threshold determination [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Zhu,

Liu,

Lou

et al. 2024

Processes

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This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance.

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Section: Introductionmentioning

confidence: 99%

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Zhu,

Liu,

Lou

et al. 2024

Processes

View full text Add to dashboard Cite

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Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Wang,

Deprizon,

Peng

et al. 2023

J Appl Eng Science

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Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

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Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

Wang,

Deprizon,

Kit

et al. 2024

J Appl Eng Science

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Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

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A Novel Dynamic Process Monitoring Algorithm: Dynamic Orthonormal Subspace Analysis

Cited by 3 publications

References 30 publications

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Process monitoring in hybrid electric vehicles based on dynamic nonlinear method

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