Open Access System for Information Sharing

Login Library

 

Article
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Fault Detection and Identification Method using Observer-based Residuals

Title
Fault Detection and Identification Method using Observer-based Residuals
Authors
JEONG, HAE DONGPARK, BUM SOOPARK, SEUNG TAEMIN, HYUNG CHEOLLEE, SEUNG CHUL
POSTECH Authors
LEE, SEUNG CHUL
Date Issued
Apr-2019
Publisher
ELSEVIER
Abstract
Manufacturing machinery is becoming increasingly complicated, and machinery breakdowns not only reduce efficiency, but also pose safety hazards. Due to the needs for maintaining high reliability within facility operation, various methods for condition monitoring are suggested as the importance of maintenance has increased. Among the various prognostics and health management (PHM) techniques, this paper introduces a model-based fault detection and isolation (FDI) technique for the diagnosis of machine health conditions. The proposed approach identifies faults by extracting fault signal information such as the magnitude or shape of the fault based on a defined relationship between a fault signal and observer theory. To validate the proposed method, a numerical simulation is conducted to demonstrate its fault detection and identification capabilities in various situations. The proposed method and data-driven methods are then compared with regard to their fault diagnosis performance.
Keywords
Condition monitoring; Machinery; Numerical methods; Signal processing; Data-driven methods; Diagnosis performance; Facility operations; Fault detection and identification; High reliability; Machinery breakdown; Model-based fault detection; Prognostics and health managements; Fault detection
URI
http://oasis.postech.ac.kr/handle/2014.oak/94895
DOI
10.1016/j.ress.2018.02.007
ISSN
0951-8320
Article Type
Article
Citation
Reliability Engineering and System Safety, vol. 184, page. 27 - 40, 2019-04
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric

Views & Downloads

Browse