Process monitoring using variational autoencoder for high-dimensional nonlinear processes

Lee, Seulki; Kwak, Mingu; Tsui, Kwok Leung; Kim, Seoung Bum

doi:10.1016/j.engappai.2019.04.013

Cited by 132 publications

(45 citation statements)

References 35 publications

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“…Usually the negative expected log-likelihood (e.g., the crossentropy function) is used ( [30], [31], [33]- [35]) but the mean squared error [32] can also be used. The second term L KL (equ.…”

Section: Variational Autoencodersmentioning

confidence: 99%

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Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

et al. 2020

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Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier.

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Section: Variational Autoencodersmentioning

confidence: 99%

“…In nonlinear processes monitoring, VAE have been recently used for high-dimensional process fault diagnosis. The most relevant characteristics of the process are extracted by the latent variable space by projecting the high-dimensional process data into a lower-dimensional space [8], [29], [31], [32], [34], [39]- [42].…”

Section: Variational Autoencodersmentioning

confidence: 99%

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

et al. 2020

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“…In 2019, variational autoencoders have been widely used to analyze different kind of signals and monitoring them [22,23]. In addition, in Zemouri et al [24], variational autoencoders have been used for train a model as a 2D visualization tool for partial discharge source classification.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

García-Ordás

Benítez-Andrades

García-Rodríguez

et al. 2020

Sensors

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The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

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“…Deep generative models (DGMs) are part of the deep models family and are a powerful way to learn any distribution of observed data through unsupervised learning. The DGMs are composed mainly by variational autoencoders (VAEs) [1][2][3][4], and generative adversarial networks (GANs) [5]. The VAEs are mainly used to extract features from the input vector in an unsupervised way while the GANs are used to generate synthetic samples through an adversarial learning by achieving an equilibrium between a Generator and a Discriminator.…”

Section: Introductionmentioning

confidence: 99%

“…The VAEs have met with great success in recent years in several applicative areas including anomaly detection [6][7][8][9], text classification [10], sentence generation [11], speech synthesis and recognition [12][13][14], spatio-temporal solar irradiance forecasting [15] and in geoscience for data assimilation [2]. In other respects, the two major application areas of the VAEs are the biomedical and healthcare recommendation [16][17][18][19], and industrial applications for nonlinear processes monitoring [1,3,4,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Adversarial Variational Autoencoder

Zemouri

2020

MAKE

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We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE.

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Process monitoring using variational autoencoder for high-dimensional nonlinear processes

Cited by 132 publications

References 35 publications

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Semi-Supervised Adversarial Variational Autoencoder

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