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Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization

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Title: Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization
Authors: Sahin, Ferat1 feseee@rit.edu, Yavuz, M. Çetin1 yavuzmc@gmail.com, Arnavut, Ziya2 ziya.arnavut@fredonia.edu, Uluyol, Önder3 onder.uluyol@honeywell.com
Source: Parallel Computing. Mar2007, Vol. 33 Issue 2, p124-143. 20p.
Subjects: Parallel computer software, Airplane motors, Bayesian field theory, Algorithms, Engineering databases, Information storage & retrieval systems
Abstract: This paper presents a fault diagnosis system for airplane engines using Bayesian networks (BN) and distributed particle swarm optimization (PSO). The PSO is inherently parallel, works for large domains and does not trap into local maxima. We implemented the algorithm on a computer cluster with 48 processors using message passing interface (MPI) in Linux. Our implementation has the advantages of being general, robust, and scalable. Unlike existing BN-based fault diagnosis methods, neither expert knowledge nor node ordering is necessary prior to the Bayesian Network discovery. The raw datasets obtained from airplane engines during actual flights are preprocessed using equal frequency binning histogram and used to generate Bayesian networks fault diagnosis for the engines. We studied the performance of the distributed PSO algorithm and generated a BN that can detect faults in the test data successfully. [Copyright &y& Elsevier]
Copyright of Parallel Computing is the property of Elsevier B.V. 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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Header DbId: egs
DbLabel: Engineering Source
An: 24299318
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization
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  Data: <searchLink fieldCode="JN" term="%22Parallel+Computing%22">Parallel Computing</searchLink>. Mar2007, Vol. 33 Issue 2, p124-143. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Parallel+computer+software%22">Parallel computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Airplane+motors%22">Airplane motors</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+field+theory%22">Bayesian field theory</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+databases%22">Engineering databases</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink>
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  Data: This paper presents a fault diagnosis system for airplane engines using Bayesian networks (BN) and distributed particle swarm optimization (PSO). The PSO is inherently parallel, works for large domains and does not trap into local maxima. We implemented the algorithm on a computer cluster with 48 processors using message passing interface (MPI) in Linux. Our implementation has the advantages of being general, robust, and scalable. Unlike existing BN-based fault diagnosis methods, neither expert knowledge nor node ordering is necessary prior to the Bayesian Network discovery. The raw datasets obtained from airplane engines during actual flights are preprocessed using equal frequency binning histogram and used to generate Bayesian networks fault diagnosis for the engines. We studied the performance of the distributed PSO algorithm and generated a BN that can detect faults in the test data successfully. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Parallel Computing is the property of Elsevier B.V. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=24299318
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        Value: 10.1016/j.parco.2006.11.005
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 124
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      – SubjectFull: Parallel computer software
        Type: general
      – SubjectFull: Airplane motors
        Type: general
      – SubjectFull: Bayesian field theory
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      – SubjectFull: Algorithms
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      – SubjectFull: Engineering databases
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      – SubjectFull: Information storage & retrieval systems
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      – TitleFull: Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization
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              Text: Mar2007
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              Y: 2007
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