Monitoring and Failure Diagnosis of a Steel Strip Process
Document identifier: oai:dalea.du.se:2661
Keyword: Grey box modelling,
Failure detection,
Supervision,
Steel IndustryPublication year: 1998Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: This paper deals with condition monitoring and failure diagnosis of a steel strip rinsing process. Modelling and identification of the process is based on a priori knowledge about the process and data from the process. Some parts of the process wear out slowly. It is not possible to measure the wear with any transducers. In the model, the worn parts are modelled explicitly and estimated on-line by an Extended Kalman Filter. The parameter estimation is used for supervision and as an advisory system for the process operators to decide which worn parts should be changed at the next planned stop. In addition to the normal wear, other types of abrupt failures may suddenly occur. It is not possible to detect these failures directly and the failures will give a biased parameter estimate and mislead the process operators into thinking, that a part subject to wear should be changed although it is performing well. Therefore, the condition monitoring system is complemented with a fault detection and diagnosis system, which distinguishes normal wear from sudden abrupt failures.
Authors
Björn Sohlberg
Högskolan Dalarna; Elektroteknik
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header:
identifier: oai:dalea.du.se:2661
datestamp: 2021-04-15T12:53:01Z
setSpec: SwePub-du
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recordContentSource: du
recordCreationDate: 2007-04-04
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-2661
titleInfo:
@attributes:
lang: eng
title: Monitoring and Failure Diagnosis of a Steel Strip Process
abstract: This paper deals with condition monitoring and failure diagnosis of a steel strip rinsing process. Modelling and identification of the process is based on a priori knowledge about the process and data from the process. Some parts of the process wear out slowly. It is not possible to measure the wear with any transducers. In the model the worn parts are modelled explicitly and estimated on-line by an Extended Kalman Filter. The parameter estimation is used for supervision and as an advisory system for the process operators to decide which worn parts should be changed at the next planned stop. In addition to the normal wear other types of abrupt failures may suddenly occur. It is not possible to detect these failures directly and the failures will give a biased parameter estimate and mislead the process operators into thinking that a part subject to wear should be changed although it is performing well. Therefore the condition monitoring system is complemented with a fault detection and diagnosis system which distinguishes normal wear from sudden abrupt failures.
subject:
@attributes:
lang: eng
topic: Grey box modelling
@attributes:
lang: eng
topic: Failure detection
@attributes:
lang: eng
topic: Supervision
@attributes:
lang: eng
topic: Steel Industry
language:
languageTerm: eng
genre:
publication/journal-article
ref
note:
Published
1
name:
@attributes:
type: personal
authority: du
namePart:
Sohlberg
Björn
role:
roleTerm: aut
affiliation:
Högskolan Dalarna
Elektroteknik
nameIdentifier: bso
originInfo:
dateIssued: 1998
publisher: IEEE
relatedItem:
@attributes:
type: host
titleInfo:
title: IEEE Transactions on Control Systems Technology
identifier:
1063-6536
1558-0865
part:
detail:
@attributes:
type: volume
number: 6
@attributes:
type: issue
number: 2
extent:
start: 294
end: 303
physicalDescription:
form: print
typeOfResource: text