Missing Data Imputation for Real Time-series Data in a Steel Industry using Generative Adversarial Networks

Sarda, Kisan; Yerudkar, Amol; Vecchio, Carmen Del

doi:10.1109/iecon48115.2021.9589716

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Here, D(x) represents the discriminator's output for real data, D(G(z)) is the discriminator's output for generated data, and z is a random noise vector [57], [58].…”

Section: Data Expansion Techniquesmentioning

confidence: 99%

Overview of Data Augmentation Techniques in Time Series Analysis

ANNAKI,

RAHMOUNE,

BOURHALEB

2024

IJACSA

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Time series data analysis is vital in numerous fields, driven by advancements in deep learning and machine learning. This paper presents a comprehensive overview of data augmentation techniques in time series analysis, with a specific focus on their applications within deep learning and machine learning. We commence with a systematic methodology for literature selection, curating 757 articles from prominent databases. Subsequent sections delve into various data augmentation techniques, encompassing traditional approaches like interpolation and advanced methods like Synthetic Data Generation, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). These techniques address complexities inherent in time series data. Moreover, we scrutinize limitations, including computational costs and overfitting risks. However, it's essential to note that our analysis does not end with limitations. We also comprehensively analyzed the advantages and applicability of the techniques under consideration. This holistic evaluation allows us to provide a balanced perspective. In summary, this overview illuminates data augmentation's role in time series analysis within deep and machine-learning contexts. It provides valuable insights for researchers and practitioners, advancing these fields and charting paths for future exploration.

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“…Here, D(x) represents the discriminator's output for real data, D(G(z)) is the discriminator's output for generated data, and z is a random noise vector [57], [58].…”

Section: Data Expansion Techniquesmentioning

confidence: 99%

Overview of Data Augmentation Techniques in Time Series Analysis

ANNAKI,

RAHMOUNE,

BOURHALEB

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Li et al [27] proposed an improved invertible auxiliary classifier GAN to enhance the extraction capabilities of feature images in echo signals, thereby improving the quality of generated samples. Sarda et al [28] introduced the GAN framework for data interpolation and synthesis, improving the quality of datasets through generated samples. Zhang et al [29] presented a conditional variational AE GAN to merge and generate signals from multiple sources, effectively addressing the issues of insufficient and imbalanced samples in fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

Liu,

Qi,

et al. 2024

Meas. Sci. Technol.

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The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, Deep Digital Twins with Information Gain Stacking Sparse Autoencoders(DDT-IGSSAE).Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes Information Gain Ratio (IGR) to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a Stacked Sparse Autoencoder (SSAE) fault classification network is designed, incorporating multi-level feature compression to enhance the model's stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method's effectiveness with a diagnostic accuracy of 98.4%.

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“…Conversely, the compression mode aligns data with a longer length to data with a shorter length using data division or segmenting. Reference [25] introduced a generative adversarial network (GAN) framework to generate synthetic data pertaining to data imputation. In [26], a multivariate time-series generative adversarial network was proposed for multivariate time-series distribution modeling by introducing multi-channel convolution into GANs; this is a mathematical approach that does not consider the properties and effects of underlying physical quantities, which can lead to differences between the data matrix and the actual system.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Data Modeling via Temporal and Spatial Alignment

Zhang,

Sun,

Zhang

2023

Processes

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It is important for data modeling to comply with a data observation window of physical variables behind the data. In this paper, a multivariate data alignment method is proposed to follow different time scales and different role effects. First, the length of the sliding windows is determined by the frequency characteristics of the time-series reconstruction. Then, the time series is aligned to the length of the window by a sequence-to-sequence neural network. This neural network is trained by replacing the loss function with dynamic time warping (DTW) in order to prevent the losses of the time series. Finally, the attention mechanism is introduced to adjust the effect of different variables, which ensures that the data model of the matrix is in accord with the intrinsic relation of the actual system. The effectiveness of the approach is demonstrated and validated by the Tennessee Eastman (TE) model.

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Missing Data Imputation for Real Time-series Data in a Steel Industry using Generative Adversarial Networks

Cited by 9 publications

References 14 publications

Overview of Data Augmentation Techniques in Time Series Analysis

Overview of Data Augmentation Techniques in Time Series Analysis

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

An Approach to Data Modeling via Temporal and Spatial Alignment

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