Journal ArticleDOI
A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets
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TLDR
A small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs) is proposed and can be well trained by using only the generated data in the condition of small fault data sample.Abstract:
The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a rough fault data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, artificial intelligence (AI)-based WT fault detection models can be well trained by using only the generated data in the condition of small fault data sample. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted using real WT data collected from a wind farm in northern China. The results indicate that the method proposed in this paper is effective.read more
Citations
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A Review on Deep Learning Applications in Prognostics and Health Management
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Journal ArticleDOI
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
None Fitri S. Kasim,Tianci Zhang,Jinglong Chen,Fudong Li,Kaiyu Zhang,Haixin Lv,Shuilong He,Enyong Xu +7 more
TL;DR: In this paper, a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
Journal ArticleDOI
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
TL;DR: In this paper , a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: The maximal information coefficient (MIC) as mentioned in this paper is a measure of dependence for two-variable relationships that captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function.
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A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series
TL;DR: This paper proposes a novel anomaly detection approach based on generative adversarial networks (GAN) to overcome the problem of class-imbalanced problems, where the number of normal samples is far larger than that of abnormal cases.
References
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
Detecting Novel Associations in Large Data Sets
David N. Reshef,David N. Reshef,David N. Reshef,Yakir A. Reshef,Yakir A. Reshef,Hilary K. Finucane,Sharon R. Grossman,Sharon R. Grossman,Gilean McVean,Gilean McVean,Peter J. Turnbaugh,Eric S. Lander,Eric S. Lander,Eric S. Lander,Michael Mitzenmacher,Pardis C. Sabeti,Pardis C. Sabeti +16 more
TL;DR: A measure of dependence for two-variable relationships: the maximal information coefficient (MIC), which captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination of the data relative to the regression function.
Proceedings ArticleDOI
Learning from Simulated and Unsupervised Images through Adversarial Training
TL;DR: SimGAN as mentioned in this paper uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors, and achieves state-of-the-art results on the MPIIGaze dataset without any labeled real data.
Posted Content
Learning from Simulated and Unsupervised Images through Adversarial Training
TL;DR: This work develops a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors, and makes several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training.
Journal ArticleDOI
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Xuewu Dai,Zhiwei Gao +1 more
TL;DR: An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are presented to reveal the future development direction in this field.
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