AutoEncoder based high-dimensional data fault detection system

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Industrial Informatics (INDIN)
PublisherIEEE
Pages1001-1006
ISBN (Electronic)978-1-5386-0837-1
Publication statusPublished - Jul 2017

Conference

Title15th IEEE International Conference on Industrial Informatics, INDIN 2017
PlaceGermany
CityEmden
Period24 - 26 July 2017

Abstract

In this paper, we propose a novel fault detection method for multivariate industrial processes. The method is based on AutoEncoder. AutoEncoder is a single-hidden-layer neural network that can learn low-dimensional nonlinear representations for high-dimensional data. In the proposed fault detection method, offline normal data are used to train an AutoEncoder, which is then used for online fault detection. The proposed method is compared with conventional methods on Tennessee Eastman process. The experimental results show that the proposed method is able to outperform other methods.

Citation Format(s)

AutoEncoder based high-dimensional data fault detection system. / Fan, Jicong; Wang, Wei; Zhang, Haijun.

Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics (INDIN). IEEE, 2017. p. 1001-1006 8104910.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)