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Measuring system entropy with a deep recurrent neural network model

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conference contribution
posted on 04.08.2021, 08:24 by Miguel Martinez-GarciaMiguel Martinez-Garcia, Eve ZhangEve Zhang, Kenji Suzuki, Yudong Zhang
In this paper, a methodology for assessing the unpredictability of systems with memory was developed. The proposed approach consists in approximating the probability distribution exhibited by the response of a system, understood as a stochastic process, with a deep recurrent neural network; such networks offer increased forecasting capability by exploiting an accumulative register of previous system states. Once the probability distribution is computed, the uncertainty or entropy of the underlying process is measured. This measure determines the degree of regularity in the system, and identifies how atypical the system dynamics are. The proposed model was validated by identifying industrial gas turbine engine faults from recorded sensor data.

Funding

EPSRC Grant EVES (EP/R029741/1)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

Pages

1253 - 1256

Source

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-01-30

Copyright date

2019

ISBN

9781728129273

eISSN

2378-363X

Language

en

Location

Helsinki, Finland

Event dates

22nd July 2019 - 25th July 2019

Depositor

Dr Eve Zhang. Deposit date: 29 July 2021