Data driven weighted estimation error benchmarking for estimators and condition monitoring systems

Research output: Contribution to journalArticle

Abstract

A simple method of benchmarking filters, predictors, smoothers or condition monitoring estimators is presented, which can avoid the need for system model knowledge. A weighted least-squares estimation problem is established, where the solution is shown to involve a term that is independent of the choice of estimator and a term that can be set to zero when using the optimal estimator. The minimum estimation error cost is therefore dependent upon the independent term in the expression and these may be computed using a simple online least-squares algorithm. The level of suboptimality, reflected in the estimation error power is then readily calculable. This enables the quality of estimation to be determined for systems which may not be completely known. If an estimator is used for condition monitoring and fault detection, the benchmark enables the deterioration in the quality of estimation to be determined. It is then possible to judge when fault estimates are sufficiently reliable. Moreover, if the system is nonlinear and fault estimators are defined for different operating conditions, then the benchmark measure can be used online to determine which estimator is best and whether the estimate is optimal in a small signal change sense.
LanguageEnglish
Pages511-521
Number of pages11
JournalIEE Proceedings Control Theory and Applications
Volume151
Issue number4
DOIs
Publication statusPublished - 2004

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Condition monitoring
Benchmarking
estimators
Error analysis
Fault detection
Deterioration
Nonlinear systems
fault detection
estimates
nonlinear systems
deterioration
Costs
costs
filters
predictions

Keywords

  • benchmark testing
  • condition monitoring
  • control system analysis
  • fault diagnosis
  • filtering theory
  • least squares approximations

Cite this

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title = "Data driven weighted estimation error benchmarking for estimators and condition monitoring systems",
abstract = "A simple method of benchmarking filters, predictors, smoothers or condition monitoring estimators is presented, which can avoid the need for system model knowledge. A weighted least-squares estimation problem is established, where the solution is shown to involve a term that is independent of the choice of estimator and a term that can be set to zero when using the optimal estimator. The minimum estimation error cost is therefore dependent upon the independent term in the expression and these may be computed using a simple online least-squares algorithm. The level of suboptimality, reflected in the estimation error power is then readily calculable. This enables the quality of estimation to be determined for systems which may not be completely known. If an estimator is used for condition monitoring and fault detection, the benchmark enables the deterioration in the quality of estimation to be determined. It is then possible to judge when fault estimates are sufficiently reliable. Moreover, if the system is nonlinear and fault estimators are defined for different operating conditions, then the benchmark measure can be used online to determine which estimator is best and whether the estimate is optimal in a small signal change sense.",
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AB - A simple method of benchmarking filters, predictors, smoothers or condition monitoring estimators is presented, which can avoid the need for system model knowledge. A weighted least-squares estimation problem is established, where the solution is shown to involve a term that is independent of the choice of estimator and a term that can be set to zero when using the optimal estimator. The minimum estimation error cost is therefore dependent upon the independent term in the expression and these may be computed using a simple online least-squares algorithm. The level of suboptimality, reflected in the estimation error power is then readily calculable. This enables the quality of estimation to be determined for systems which may not be completely known. If an estimator is used for condition monitoring and fault detection, the benchmark enables the deterioration in the quality of estimation to be determined. It is then possible to judge when fault estimates are sufficiently reliable. Moreover, if the system is nonlinear and fault estimators are defined for different operating conditions, then the benchmark measure can be used online to determine which estimator is best and whether the estimate is optimal in a small signal change sense.

KW - benchmark testing

KW - condition monitoring

KW - control system analysis

KW - fault diagnosis

KW - filtering theory

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