Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR.
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| Title: | Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR. |
|---|---|
| Authors: | Kumar, Swetha R1 (AUTHOR) srk.ice@psgtech.ac.in, Devakumar, Jayaprasanth1 (AUTHOR) djp.ice@psgtech.ac.in |
| Source: | Progress in Nuclear Energy. Sep2023, Vol. 163, pN.PAG-N.PAG. 1p. |
| Subjects: | Recurrent neural networks, Sensor networks, Pressurized water reactors, Nonlinear systems, Nuclear models |
| Abstract: | Real-time monitoring of sensors is crucial in attaining consistent process performance with strict safety and eco-friendly measures. This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. A Recurrent Neural Network (RNN) is utilized to estimate state variables with the inputs and outputs of the process. RNNs build predictive models of the process. Then, a bank of residuals of state variables is obtained such that each residual is responsive to a subsection of faults and unresponsive to others. Consequently, an exclusive fault signature is attained for each fault case. One of the merits of the proposed procedure is that it does not necessitate the fault history of the process or first principle models contrasting to other existing outcomes in the literature. The efficacy of the proposed FDI methodology is studied on a highly nonlinear pressurized water nuclear reactor model. • This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. • A Recurrent Neural Network (RNN) with 10 hidden neurons is utilized to estimate state variables with the inputs and outputs of the process. • The effectiveness of the designed recurrent neural network model for fault detection and isolation is studied under five different sensor faults scenarios: drift, erratic, hard-over, spike and stuck. • Upon analyzing the residuals, a structure matrix is formed based on the fault signature. It was noted that the sensor fault in the estimated state variables of RNN leaves a fault signature of '1' in its corresponding residual only, whereas the sensor fault on the input variable of RNN leaves a fault signature of '1' in all other state residuals except its corresponding residual [ABSTRACT FROM AUTHOR] |
| Copyright of Progress in Nuclear Energy 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 170904864 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Swetha+R%22">Kumar, Swetha R</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> srk.ice@psgtech.ac.in</i><br /><searchLink fieldCode="AR" term="%22Devakumar%2C+Jayaprasanth%22">Devakumar, Jayaprasanth</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> djp.ice@psgtech.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Progress+in+Nuclear+Energy%22">Progress in Nuclear Energy</searchLink>. Sep2023, Vol. 163, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Pressurized+water+reactors%22">Pressurized water reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+models%22">Nuclear models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Real-time monitoring of sensors is crucial in attaining consistent process performance with strict safety and eco-friendly measures. This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. A Recurrent Neural Network (RNN) is utilized to estimate state variables with the inputs and outputs of the process. RNNs build predictive models of the process. Then, a bank of residuals of state variables is obtained such that each residual is responsive to a subsection of faults and unresponsive to others. Consequently, an exclusive fault signature is attained for each fault case. One of the merits of the proposed procedure is that it does not necessitate the fault history of the process or first principle models contrasting to other existing outcomes in the literature. The efficacy of the proposed FDI methodology is studied on a highly nonlinear pressurized water nuclear reactor model. • This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. • A Recurrent Neural Network (RNN) with 10 hidden neurons is utilized to estimate state variables with the inputs and outputs of the process. • The effectiveness of the designed recurrent neural network model for fault detection and isolation is studied under five different sensor faults scenarios: drift, erratic, hard-over, spike and stuck. • Upon analyzing the residuals, a structure matrix is formed based on the fault signature. It was noted that the sensor fault in the estimated state variables of RNN leaves a fault signature of '1' in its corresponding residual only, whereas the sensor fault on the input variable of RNN leaves a fault signature of '1' in all other state residuals except its corresponding residual [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Progress in Nuclear Energy 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.pnucene.2023.104836 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Sensor networks Type: general – SubjectFull: Pressurized water reactors Type: general – SubjectFull: Nonlinear systems Type: general – SubjectFull: Nuclear models Type: general Titles: – TitleFull: Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Swetha R – PersonEntity: Name: NameFull: Devakumar, Jayaprasanth IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 01491970 Numbering: – Type: volume Value: 163 Titles: – TitleFull: Progress in Nuclear Energy Type: main |
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