Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults
This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelli...
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MDPI AG
2019-02-01
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Series: | Energies |
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Online Access: | https://www.mdpi.com/1996-1073/12/4/726 |
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author | Xiaochuan Li David Mba Demba Diallo Claude Delpha |
author_facet | Xiaochuan Li David Mba Demba Diallo Claude Delpha |
author_sort | Xiaochuan Li |
collection | DOAJ |
description | This study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>T</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> </mrow> </semantics> </math> </inline-formula> and a CVR-based monitoring index, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula>. A CVR-based contribution plot approach is also proposed based on <inline-formula> <math display="inline"> <semantics> <mi>Q</mi> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula> statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor. |
first_indexed | 2024-04-14T06:34:49Z |
format | Article |
id | doaj.art-c47d023757514892924279d7f1cccc7b |
institution | Directory Open Access Journal |
issn | 1996-1073 |
language | English |
last_indexed | 2024-04-14T06:34:49Z |
publishDate | 2019-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Energies |
spelling | doaj.art-c47d023757514892924279d7f1cccc7b2022-12-22T02:07:30ZengMDPI AGEnergies1996-10732019-02-0112472610.3390/en12040726en12040726Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving FaultsXiaochuan Li0David Mba1Demba Diallo2Claude Delpha3Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UKFaculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UKLaboratoire Génie Electrique et Électronique de Paris (GeePs), CNRS, CentraleSupélec, Université Paris-Sud, 91190 Gif Sur Yvette, FranceLaboratoire des Signaux et Systèmes (L2S), CNRS, CentraleSupélec, Université Paris-Sud, 91192 Gif Sur Yvette, FranceThis study puts forward a novel diagnostic approach based on canonical variate residuals (CVR) to implement incipient fault diagnosis for dynamic process monitoring. The conventional canonical variate analysis (CVA) fault detection approach is extended to form a new monitoring index based on Hotelling’s <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>T</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, <inline-formula> <math display="inline"> <semantics> <mrow> <mi>Q</mi> </mrow> </semantics> </math> </inline-formula> and a CVR-based monitoring index, <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula>. A CVR-based contribution plot approach is also proposed based on <inline-formula> <math display="inline"> <semantics> <mi>Q</mi> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> </inline-formula> statistics. Two performance metrics: (1) false alarm rate and (2) missed detection rate are used to assess the effectiveness of the proposed approach. The CVR diagnostic approach was validated on incipient faults in a continuous stirred tank reactor (CSTR) system and an operational centrifugal compressor.https://www.mdpi.com/1996-1073/12/4/726slowly evolving faultsfault detectionfault identification |
spellingShingle | Xiaochuan Li David Mba Demba Diallo Claude Delpha Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults Energies slowly evolving faults fault detection fault identification |
title | Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults |
title_full | Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults |
title_fullStr | Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults |
title_full_unstemmed | Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults |
title_short | Canonical Variate Residuals-Based Fault Diagnosis for Slowly Evolving Faults |
title_sort | canonical variate residuals based fault diagnosis for slowly evolving faults |
topic | slowly evolving faults fault detection fault identification |
url | https://www.mdpi.com/1996-1073/12/4/726 |
work_keys_str_mv | AT xiaochuanli canonicalvariateresidualsbasedfaultdiagnosisforslowlyevolvingfaults AT davidmba canonicalvariateresidualsbasedfaultdiagnosisforslowlyevolvingfaults AT dembadiallo canonicalvariateresidualsbasedfaultdiagnosisforslowlyevolvingfaults AT claudedelpha canonicalvariateresidualsbasedfaultdiagnosisforslowlyevolvingfaults |