09133510.pdf (1.99 MB)
Download file

Deep recurrent entropy adaptive model for system reliability monitoring

Download (1.99 MB)
journal contribution
posted on 01.10.2020, 12:47 authored by Miguel Martinez-GarciaMiguel Martinez-Garcia, Yu Zhang, Kenji Suzuki, Yu-Dong Zhang
The aim of this paper is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with a deep recurrent neural network; such networks are capable of exploiting the memory in the system for enhanced forecasting capability. Once the probability distribution is retrieved, the entropy or missing information about the underlying process is computed, which is interpreted as the uncertainty with respect to the system’s behaviour. Hence the model identifies how far the system dynamics are from its typical response, in order to evaluate the system reliability and to predict system faults and/or normal accidents. The validity of the model is verified with sensor data recorded from commissioning gas turbines, belonging to normal and faulty conditions.

Funding

Evolutionary Virtual Expert System

Engineering and Physical Sciences Research Council

Find out more...

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

2

Pages

839 - 848

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

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.

Acceptance date

29/06/2020

Publication date

2020-07-06

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Dr Miguel Martinez Garcia. Deposit date: 30 September 2020