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Title: Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems

Abstract

Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system. Specifically, acting controller in any closed loop dynamical system, works to reduce the effect of system level faults. A novel Generative Adversarial based deep Autoencoder is designed to classify datasets under normal and faulty operating conditions. This proposed network performs significantly well when compared to any available classifier based methods, and moreover, does not require labeled fault incorporated datasets for training purpose. Finally, this aforementioned network's performance is tested on a high complexity building energy system dataset.

Authors:
ORCiD logo [1];  [2];  [1]
  1. BATTELLE (PACIFIC NW LAB)
  2. University of Florida
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1526303
Report Number(s):
PNNL-SA-134059
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: 29th International Workshop on Principles of Diagnosis (DX'18), August 27-30, 2018, Warsaw, Poland
Country of Publication:
Germany
Language:
English
Subject:
deep learning, Active fault detection

Citation Formats

Chakraborty, Indrasis, Chakraborty, Rudrasis, and Vrabie, Draguna L. Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems. Germany: N. p., 2018. Web.
Chakraborty, Indrasis, Chakraborty, Rudrasis, & Vrabie, Draguna L. Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems. Germany.
Chakraborty, Indrasis, Chakraborty, Rudrasis, and Vrabie, Draguna L. Tue . "Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems". Germany.
@article{osti_1526303,
title = {Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems},
author = {Chakraborty, Indrasis and Chakraborty, Rudrasis and Vrabie, Draguna L.},
abstractNote = {Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system. Specifically, acting controller in any closed loop dynamical system, works to reduce the effect of system level faults. A novel Generative Adversarial based deep Autoencoder is designed to classify datasets under normal and faulty operating conditions. This proposed network performs significantly well when compared to any available classifier based methods, and moreover, does not require labeled fault incorporated datasets for training purpose. Finally, this aforementioned network's performance is tested on a high complexity building energy system dataset.},
doi = {},
journal = {},
number = ,
volume = ,
place = {Germany},
year = {2018},
month = {5}
}

Conference:
Other availability
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