Neural Network Modelling Applied for Model-Based Fault Detection

Zhanqun Shi, Yibo Fan, Fengshou Gu, Abdul Hannan Ali, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper aims to combine neural network modelling with model-based fault detection. An accurate and robust model is critical in model-based fault detection. However, the development of such a model is the most difficult task especially when a non-linear system is involved. The problem comes not only from the lack of concerned information about model parameters, but also from the inevitable linearization. In order to solve this problem, neural networks are introduced in this paper. Instead of using conventional neural network modelling, the neural network is only used to approximate the non-linear part of the system, leaving the linear part to be represented by a mathematical model. This new scheme of integration between neural network and mathematical model (NNMM) allows the compensation of the error from conventional modelling methods. Simultaneously, it keeps the residual signatures physically interpretable.

Original languageEnglish
Title of host publicationASME 7th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2004
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages149-155
Number of pages7
Volume3
ISBN (Electronic)0791837416
ISBN (Print)0791841758
DOIs
Publication statusPublished - 19 Jul 2004
Externally publishedYes
Event7th Biennial Conference on Engineering Systems Design and Analysis - Manchester, United Kingdom
Duration: 19 Jul 200422 Jul 2004
Conference number: 7

Conference

Conference7th Biennial Conference on Engineering Systems Design and Analysis
CountryUnited Kingdom
CityManchester
Period19/07/0422/07/04

    Fingerprint

Cite this

Shi, Z., Fan, Y., Gu, F., Ali, A. H., & Ball, A. (2004). Neural Network Modelling Applied for Model-Based Fault Detection. In ASME 7th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2004 (Vol. 3, pp. 149-155). [ESDA2004-58197] American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/ESDA2004-58197