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Effective fault diagnosis based on strong tracking UKF.

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Title: Effective fault diagnosis based on strong tracking UKF.
Authors: Han, Pengxin, Mu, Rongjun, Cui, Naigang
Source: Aircraft Engineering & Aerospace Technology. 2011, Vol. 83 Issue 5, p275-282. 8p.
Subjects: Kalman filtering, Chi-squared test, Navigation, Rocket engines, Flight testing
Abstract: Purpose – The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault-tolerant re-entry navigation requirements of reusable boost vehicle (RBV). Design/methodology/approach – A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ2-test-based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors. Findings – The comparisons with χ2-test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method. Practical implications – This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles. Originality/value – A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV. [ABSTRACT FROM AUTHOR]
Copyright of Aircraft Engineering & Aerospace Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 72922755
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
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  Data: Effective fault diagnosis based on strong tracking UKF.
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Han%2C+Pengxin%22">Han, Pengxin</searchLink><br /><searchLink fieldCode="AR" term="%22Mu%2C+Rongjun%22">Mu, Rongjun</searchLink><br /><searchLink fieldCode="AR" term="%22Cui%2C+Naigang%22">Cui, Naigang</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Aircraft+Engineering+%26+Aerospace+Technology%22">Aircraft Engineering & Aerospace Technology</searchLink>. 2011, Vol. 83 Issue 5, p275-282. 8p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Navigation%22">Navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Rocket+engines%22">Rocket engines</searchLink><br /><searchLink fieldCode="DE" term="%22Flight+testing%22">Flight testing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose – The purpose of this paper is to address the flaws of traditional methods and fulfil the special fault-tolerant re-entry navigation requirements of reusable boost vehicle (RBV). Design/methodology/approach – A kind of improved estimation method based on strong tracking unscented Kalman filter (STUKF) is put forward. According to the fact that the traditional state χ2-test-based fault diagnosis method is incompetent to detect the signal point small jerks and slowly varying fault in the measurement, a kind of original fault diagnosis technology based on STUKF is used to check the working states of navigation sensors. Findings – The comparisons with χ2-test method under typical failure distributions validate the perfect state tracking and fault diagnosis performances of this improved method. Practical implications – This kind of state estimation and fault diagnosis method could be used in the navigation and guidance systems for many kinds of aeronautical and astronautical vehicles. Originality/value – A kind of novel strong tracking state estimation filter is used, and a kind of very effective fault diagnosis criterion is put forward for the navigation of RBV. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Aircraft Engineering & Aerospace Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1108/00022661111159889
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 275
    Subjects:
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Chi-squared test
        Type: general
      – SubjectFull: Navigation
        Type: general
      – SubjectFull: Rocket engines
        Type: general
      – SubjectFull: Flight testing
        Type: general
    Titles:
      – TitleFull: Effective fault diagnosis based on strong tracking UKF.
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            NameFull: Han, Pengxin
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            NameFull: Mu, Rongjun
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            NameFull: Cui, Naigang
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            – D: 01
              M: 09
              Text: 2011
              Type: published
              Y: 2011
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              Value: 17488842
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              Value: 83
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              Value: 5
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            – TitleFull: Aircraft Engineering & Aerospace Technology
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