Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes

He, Yong; Shi, Hongbo; Tan, Shuai; Song, Bing; Zhu, Jiazhen

doi:10.1016/j.jtice.2021.04.062

Cited by 29 publications

(17 citation statements)

References 28 publications

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“…A mathematical formulation of a typical 1D-convolutional operation denoted by the symbol ( * ), shown in Figs. 1 and 2, can be expressed as follows [26,34]:…”

Section: Convolutional Autoencoders (Caes)mentioning

confidence: 99%

“…Convolutional autoencoders (CAEs) have emerged as an alternative to AEs in nonlinear compression manifolds. Their structure comprises several operations such as convolution-deconvolution, pooling-upsampling, and a multilayer perceptron layer (MLP) [26,27]. CAEs offer the possibility of sharing coefficients and local patch connections, allowing a significant reduction in the number of trainable parameters.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Abdedou

Soulaïmani

2023

Preprint

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A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems. The objective is to perform accurate and rapid uncertainty analyses of the flow outputs of interest for which the input parameters are deemed uncertain. The data are constituted from a set of high-fidelity snapshots, collected using an inhouse high-fidelity flow solver, which correspond to a sample of the uncertain input parameters. The method uses a 1D-convolutional autoencoder to reduce the spatial dimension of the unstructured meshes used by the flow solver. Another convolutional autoencoder is used for the time compression. The encoded latent vectors, generated from the two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron. The proposed model allows for rapid predictions for unseen parameter values, allowing the output statistical moments to be computed efficiently. The accuracy of the proposed approach is compared to that of the linear reduced-order technique based on an artificial neural network through two benchmark tests (the one-dimensional Burgers and Stoker’s solutions) and a hypothetical dam break flow problem, with an unstructured mesh and over a complex bathymetry river. The numerical results show that the proposed methods present strong predictive capabilities to accurately approximate the statistical moments of the outputs. In particular, the predicted statistical moments are oscillations-free, unlike those obtained with the traditional proper orthogonal decomposition method. The proposed reduction framework is simple to implement and can be applied to other parametric and timedependent problems governed by partial differential equations, which are commonly encountered in many engineering and science problems.

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“…A mathematical formulation of a typical 1D-convolutional operation denoted by the symbol ( * ), shown in Figs. 1 and 2, can be expressed as follows [26,34]:…”

Section: Convolutional Autoencoders (Caes)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Abdedou

Soulaïmani

2023

Preprint

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show abstract

“…A mathematical formulation of a typical 1d-convolutional operation denoted by the symbol ( * ), shown in Figs. 1 and 2, can be expressed as follows [26,30]:…”

Section: Convolutional Autoencoders (Caes)mentioning

confidence: 99%

“…Convolutional autoencoders (CAEs) have emerged as an alternative to AEs in the nonlinear compression manifolds. Their structure comprises several operations such as convolution-deconvolution, pooling-upsampling, and multilayer perceptron (MLP) [26,27].…”

Section: Introductionmentioning

confidence: 99%

Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders

Abdedou¹,

Soulaïmani²

2022

Preprint

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A non-intrusive reduced order model based on convolutional autoencoders (NIROM-CAEs) is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatio-temporal large-scale physical problems. The method uses two 1d-convolutional autoencoders (CAEs) to reduce the spatial and temporal dimensions from a set of high-fidelity snapshots collected from the high-fidelity numerical solver. The encoded latent vectors, generated from two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron (MLP). The accuracy of the proposed approach is compared to that of the linear reduced-order technique-based artificial neural network (POD-ANN) through two benchmark tests (the one-dimensional Burgers and Stoker's solutions) and a hypothetical dam-break flow problem, with an unstructured mesh and over a complex bathymetry river. The numerical results show that the proposed nonlinear framework presents strong predictive abilities to accurately approximate the statistical moments of the outputs for complex stochastic large-scale and time-dependent problems, with low computational cost during the predictive online stage.

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“…A lack of knowledge about energy poverty is a major factor in families' limited capacity to get energy and the resulting energy poverty. This is referred to as "energy poverty" because of the inability of developed countries' minimum energy consumption to economic development their households' economic development energy requirements (He et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Nexus Between Economic Development and Energy Poverty to Gain Energy Efficiency: Empirical Evidence From China

Anser

Yusop²,

Abbas³

et al. 2022

Front. Energy Res.

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Mediating the nexus between economic development, energy poverty, and energy efficiency has become a major issue for governments. Evidence from China shows that both sectors have an important role in determining economic development policies and alleviating poverty. Economic development in China is examined experimentally in this research. This study makes use of the Data Envelopment Analysis and the entropy approach between 2007/08 and 2010/11 on the 17 provinces of china. For every unit increase in economic growth pressure, the development indicator of energy efficiency falls by 3.4 percent. Energy poverty and energy efficiency in China are strongly linked to economic development, according to the model’s economic development findings. Economic inequalities in China have increased as a result of greater economic development or China’s economic development to be of high quality, we believe that our results will be useful in understanding the function of national economic growth management and coordination in wealth distribution and energy use. For rural and male-headed families, the economic development is more consistent. Employees are most likely to be economic developmentt from an increase in economic development, which is expected to reduce energy poverty the most and improve energy efficiency the most.

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Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes

Cited by 29 publications

References 28 publications

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders

Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders

Nexus Between Economic Development and Energy Poverty to Gain Energy Efficiency: Empirical Evidence From China

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