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Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional variational autoencoder

IEEE ACCESS 2020年 8卷 47072-47081页

作者： Chen, Tingting Liu, Xueping Xia, Bizhong Wang, Wei Lai, Yongzhi Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China

With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically detect incipient failures before robots totally shut down. Model-based methods are typically used in anomaly detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for anomaly detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised anomaly detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time anomaly detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully detect anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.

关键词： Anomaly detection industrial robots sliding window variational autoencoder convolutional neural network

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Video anomaly detection and localization via Gaussian Mixture Fully Convolutional variational autoencoder

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COMPUTER VISION AND IMAGE UNDERSTANDING 2020年 195卷 102920-102920页

作者： Fan, Yaxiang Wen, Gongjian Li, Deren Qiu, Shaohua Levine, Martin D. Xiao, Fei Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan 430033 Peoples R China Natl Univ Def Technol Sci & Technol Automat Target Recognit Lab ATR Changsha 410073 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430071 Hubei Peoples R China McGill Univ Ctr Intelligent Machines Dept Elect & Comp Engn 3480 Univ St Montreal PQ H3A 2A7 Canada

We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture variational autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-decoder structure to preserve relative spatial coordinates between the input image and the output feature map. Based on the joint probabilities of each of the Gaussian mixture components, we introduce a sample energy based method to score the anomaly of image test patches. A two-stream network framework is employed to combine the appearance and motion anomalies, using RGB frames for the former and dynamic flow images, for the latter. We test our approach on two popular benchmarks (UCSD Dataset and Avenue Dataset). The experimental results verify the superiority of our method compared to the state of the art.

关键词： Anomaly detection Video surveillance variational autoencoder Gaussian mixture model Dynamic flow Two-stream network

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Geophysical Inversion Using a variational autoencoder to Model an Assembled Spatial Prior Uncertainty

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2022年第3期127卷 e2021JB022581-e2021JB022581页

作者： Lopez-Alvis, J. Nguyen, F. Looms, M. C. Hermans, T. Univ Liege Urban & Environm Engn Appl Geophys Liege Belgium Univ Copenhagen Dept Geosci & Nat Resource Management Copenhagen Denmark Univ Ghent Dept Geol Ghent Belgium

Prior information regarding subsurface spatial patterns may be used in geophysical inversion to obtain realistic subsurface models. Field experiments require prior information with sufficiently diverse patterns to accurately estimate the spatial distribution of geophysical properties in the sensed subsurface domain. A variational autoencoder (VAE) provides a way to assemble all patterns deemed possible in a single prior distribution. Such patterns may include those defined by different base training images and also their perturbed versions, for example, those resulting from geologically consistent operations such as erosion/dilation, local deformation, and intrafacies variability. Once the VAE is trained, inversion may be done in the latent space which ensures that inverted models have the patterns defined by the assembled prior. Gradient-based inversion with both a synthetic and a field case of cross-borehole GPR traveltime data shows that using the VAE assembled prior performs as good as using the VAE trained on the pattern with the best fit, but it has the advantage of lower computation cost and more realistic prior uncertainty. Moreover, the synthetic case shows an adequate estimation of most small-scale structures. The absolute values of wave velocity are computed by assuming a linear mixing model which involves two additional parameters that effectively shift and scale velocity values and are included in the inversion.

关键词： prior information geophysical inversion variational autoencoder deep learning ground-penetrating radar traveltime tomography

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variational autoencoder Transfer Functions for Onshore Tsunami Hazard Curves

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第2期2卷

作者： Willington Renteria Patrick Lynett Maile McCann Behzad Ebrahimi Hong Kie Thio Ian Robertson Chris Siverd Betsy Hicks University of Southern California Los Angeles CA USA AECOM Los Angeles CA USA Michael Baker International Alexandria VA USA Moffatt & Nichol New York NY USA Federal Emergency Management Agency Washington DC USA

To quickly estimate tsunami hazards along the coastline, we present a data-driven transfer function method to reconstruct onshore tsunami hazard curves from offshore hazard curves with corresponding topographic and bathymetric data. The transfer function is approximated by a type of artificial neural network called a variational autoencoder (VAE). The VAE first encodes input data, including offshore hazard curves and topographic and bathymetric data. Once encoded, the data are represented by a normal distribution of latent variables. The VAE then uses a trained decoder to sample the distribution created by the latent variables and reconstruct a continuous hazard function at the onshore location. As a probabilistic distribution represents the encoded values, the resulting hazard curve output has inherent stochasticity. Thus, model variance can be found through many realizations of the transfer function for a single set of inputs. We developed a set of transfer functions to accurately predict the onshore hazard curves for (a) onshore flow depth, (b) Froude number (dimensionless velocity), and (c) dimensionless momentum flux. We construct two flow depth transfer functions with one version utilizing an “anchor point” taken from established site-specific numerical modeling data. The VAEs to predict velocity and momentum flux incorporate an approach that leverages condensed topographic information around the point of interest (topographic rings). The resulting VAE's provide estimates of tsunami hazard with accuracy sufficient to perform impact and risk assessments. Overall, the transfer function method efficiently estimates onshore tsunami hazard curves, together with model uncertainty quantification, without requiring computationally expensive numerical simulations. A data-driven transfer function uses variational autoencoder-based regression to estimate onshore tsunami hazard curves from offshore data Transfer functions allow the tsunami hazard assessment of coastal are

关键词： tsunamis PTHA variational autoencoder deep learning

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Gaussian Mixture variational autoencoder for Semi-Supervised Topic Modeling

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IEEE ACCESS 2020年 8卷 106843-106854页

作者： Zhou, Cangqi Ban, Hao Zhang, Jing Li, Qianmu Zhang, Yinghua Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Nanjing Univ Sci & Technol Sch Cyber Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Informat Dept Nanjing 210094 Peoples R China SenseDeal Intelligent Technol Co Ltd Beijing 100084 Peoples R China

Topic models are widely explored for summarizing a corpus of documents. Recent advances in variational autoencoder (VAE) have enabled the development of black-box inference methods for topic modeling in order to alleviate the drawbacks of classical statistical inference. Most existing VAE based approaches assume a unimodal Gaussian distribution for the approximate posterior of latent variables, which limits the flexibility in encoding the latent space. In addition, the unsupervised architecture hinders the incorporation of extra label information, which is ubiquitous in many applications. In this paper, we propose a semi-supervised topic model under the VAE framework. We assume that a document is modeled as a mixture of classes, and a class is modeled as a mixture of latent topics. A multimodal Gaussian mixture model is adopted for latent space. The parameters of the components and the mixing weights are encoded separately. These weights, together with partially labeled data, also contribute to the training of a classifier. The objective is derived under the Gaussian mixture assumption and the semi-supervised VAE framework. Modules of the proposed framework are appropriately designated. Experiments performed on three benchmark datasets demonstrate the effectiveness of our method, comparing to several competitive baselines.

关键词： Computational modeling Gaussian mixture model Standards Data models Gaussian distribution Training Topic model variational autoencoder semi-supervised learning Gaussian mixture model deep generative learning

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Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in variational autoencoder Based Voice Conversion

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020年第4期4卷 468-479页

作者： Huang, Wen-Chin Luo, Hao Hwang, Hsin-Te Lo, Chen-Chou Peng, Yu-Huai Tsao, Yu Wang, Hsin-Min Acad Sinica Inst Informat Sci Taipei 11529 Taiwan Nagoya Univ Grad Sch Informat Nagoya Aichi 4648601 Japan Acad Sinica Res Ctr Informat Technol Inst Informat Sci Taipei 11529 Taiwan

An effective approach for voice conversion (VC) is to disentangle linguistic content from other components in the speech signal. The effectiveness of variational autoencoder (VAE) based VC (VAE-VC), for instance, strongly relies on this principle. In our prior work, we proposed a cross-domain VAE-VC (CDVAE-VC) framework, which utilized acoustic features of different properties, to improve the performance of VAE-VC. We believed that the success came from more disentangled latent representations. In this article, we extend the CDVAE-VC framework by incorporating the concept of adversarial learning, in order to further increase the degree of disentanglement, thereby improving the quality and similarity of converted speech. More specifically, we first investigate the effectiveness of incorporating the generative adversarial networks (GANs) with CDVAE-VC. Then, we consider the concept of domain adversarial training and acid an explicit constraint to the latent representation, realized by a speaker classifier, to explicitly eliminate the speaker information that resides in the latent code. Experimental results confirm that the degree of disentanglement of the learned latent representation can he enhanced by both GANs and the speaker classifier. Meanwhile, subjective evaluation results in terms of quality and similarity scores demonstrate the effectiveness of our proposed methods.

关键词： Voice conversion unsupervised learning disentangled representation variational autoencoder adversarial learning cross domain features

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A variational autoencoder Mixture Model for Online Behavior Recommendation

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IEEE ACCESS 2020年 8卷 132736-132747页

作者： Nguyen, Minh-Duc Cho, Yoon-Sik Sejong Univ Dept Software Convergence Seoul 05006 South Korea Sejong Univ Dept Data Sci Seoul 05006 South Korea

Online behavior recommendation is an attractive research topic related to social media mining. This topic focuses on suggesting suitable behaviors for users in online platforms, including music listening, video watching, e-commerce, to name but a few to improve the user experience, an essential factor for the success of online services. A successful online behavior recommendation system should have the ability to predict behaviors that users used to performs and also suggest behaviors that users never performed before. In this paper, we develop a mixture model that contains two components to address this problem. The first component is the user-specific preference component that represents the habits of users based on their behavior history. The second component is the latent group preference component based on variational autoencoder, a deep generative neural network. This component corresponds to the hidden interests of users and allows us to discover the unseen behavior of users. We conduct experiments on various real-world datasets with different characteristics to show the performance of our model in different situations. The result indicates that our proposed model outperforms the previous mixture models for recommendation problem.

关键词： Mixture models History Probabilistic logic Data models Neural networks Task analysis Training Online behavior recommendation mixture model variational autoencoder

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Semi-Supervised Neural Chord Estimation Based on a variational autoencoder With Latent Chord Labels and Features

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 2956-2966页

作者： Wu, Yiming Carsault, Tristan Nakamura, Eita Yoshii, Kazuyoshi Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan IRCAM Representat Mus F-75004 Paris France Kyoto Univ Hakubi Ctr Adv Res Kyoto 6068501 Japan

This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availability of chord annotations. The typical approach to ACE is to train a deep classification model (neural chord estimator) in a supervised manner by using only annotated music signals. In this discriminative approach, prior knowledge about chord label sequences (model output) has scarcely been taken into account. In contrast, we propose a unified generative and discriminative approach in the framework of amortized variational inference. More specifically, we formulate a deep generative model that represents the generative process of chroma vectors (observed variables) from discrete labels and continuous features (latent variables), which are assumed to follow a Markov model favoring self-transitions and a standard Gaussian distribution, respectively. Given chroma vectors as observed data, the posterior distributions of the latent labels and features are computed approximately by using deep classification and recognition models, respectively. These three models form a variational autoencoder and can be trained jointly in a semi-supervised manner. The experimental results show that the regularization of the classification model based on the Markov prior of chord labels and the generative model of chroma vectors improved the performance of ACE even under the supervised condition. The semi-supervised learning using additional non-annotated data can further improve the performance.

关键词： Hidden Markov models Music Multiple signal classification Markov processes Computational modeling Estimation Automatic chord estimation semi-supervised learning variational autoencoder

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Remote sensing image captioning via variational autoencoder and Reinforcement Learning

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KNOWLEDGE-BASED SYSTEMS 2020年 203卷 105920-105920页

作者： Shen, Xiangqing Liu, Bing Zhou, Yong Zhao, Jiaqi Liu, Mingming China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Beijing Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Jiangsu Vocat Inst Architectural Technol Sch Intelligent Mfg Xuzhou 221008 Jiangsu Peoples R China Jiangsu Normal Univ Sch Mechatron Engn Xuzhou 221008 Jiangsu Peoples R China

Image captioning, i.e., generating the natural semantic descriptions of given image, is an essential task for machines to understand the content of the image. Remote sensing image captioning is a part of the field. Most of the current remote sensing image captioning models suffered the overfitting problem and failed to utilize the semantic information in images. To this end, we propose a variational autoencoder and Reinforcement Learning based Two-stage Multi-task Learning Model (VRTMM) for the remote sensing image captioning task. In the first stage, we finetune the CNN jointly with the variational autoencoder. In the second stage, the Transformer generates the text description using both spatial and semantic features. Reinforcement Learning is then applied to enhance the quality of the generated sentences. Our model surpasses the previous state of the art records by a large margin on all seven scores on Remote Sensing Image Caption Dataset. The experiment result indicates our model is effective on remote sensing image captioning and achieves the new state-of-the-art result. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Transformer variational autoencoder Transfer learning Remote sensing image captioning Self-attention mechanisms Convolutional neural network Reinforcement learning

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adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

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KNOWLEDGE-BASED SYSTEMS 2020年 190卷 105187-105187页

作者： Wang, Xuhong Du, Ying Lin, Shijie Cui, Ping Shen, Yuntian Yang, Yupu Shanghai Jiao Tong Univ Shanghai Peoples R China Wuhan Univ Wuhan Peoples R China Univ Calif Davis Davis CA 95616 USA

Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Moreover, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a self-adversarial variational autoencoder (adVAE) with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of a variational autoencoder, a generator G tries to distinguish between the normal latent variables encoded by E and the anomalous latent variables synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate, but also become an additional regularization mechanism to prevent overfitting. Compared with other competitive methods, the proposed model achieves significant improvements in extensive experiments. The employed datasets and our model are available in a Github repository. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Outlier detection Novelty detection Deep generative model variational autoencoder

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