Diagnosing a Strong-Fault Model by Conflict and Consistency

The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monoton...
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Gespeichert in:

Autor*in:	Wenfeng Zhang [verfasserIn] Qi Zhao [verfasserIn] Hongbo Zhao [verfasserIn] Gan Zhou [verfasserIn] Wenquan Feng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 18(2018), 4, p 1016
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:4, p 1016

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s18041016

Katalog-ID:	DOAJ08539758X

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allfields	10.3390/s18041016 doi (DE-627)DOAJ08539758X (DE-599)DOAJ57d8d4998ba54f749dc47dc3575f306a DE-627 ger DE-627 rakwb eng TP1-1185 Wenfeng Zhang verfasserin aut Diagnosing a Strong-Fault Model by Conflict and Consistency 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology Qi Zhao verfasserin aut Hongbo Zhao verfasserin aut Gan Zhou verfasserin aut Wenquan Feng verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1016 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1016 https://doi.org/10.3390/s18041016 kostenfrei https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a kostenfrei http://www.mdpi.com/1424-8220/18/4/1016 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1016
spelling	10.3390/s18041016 doi (DE-627)DOAJ08539758X (DE-599)DOAJ57d8d4998ba54f749dc47dc3575f306a DE-627 ger DE-627 rakwb eng TP1-1185 Wenfeng Zhang verfasserin aut Diagnosing a Strong-Fault Model by Conflict and Consistency 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology Qi Zhao verfasserin aut Hongbo Zhao verfasserin aut Gan Zhou verfasserin aut Wenquan Feng verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1016 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1016 https://doi.org/10.3390/s18041016 kostenfrei https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a kostenfrei http://www.mdpi.com/1424-8220/18/4/1016 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1016
allfields_unstemmed	10.3390/s18041016 doi (DE-627)DOAJ08539758X (DE-599)DOAJ57d8d4998ba54f749dc47dc3575f306a DE-627 ger DE-627 rakwb eng TP1-1185 Wenfeng Zhang verfasserin aut Diagnosing a Strong-Fault Model by Conflict and Consistency 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology Qi Zhao verfasserin aut Hongbo Zhao verfasserin aut Gan Zhou verfasserin aut Wenquan Feng verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1016 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1016 https://doi.org/10.3390/s18041016 kostenfrei https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a kostenfrei http://www.mdpi.com/1424-8220/18/4/1016 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1016
allfieldsGer	10.3390/s18041016 doi (DE-627)DOAJ08539758X (DE-599)DOAJ57d8d4998ba54f749dc47dc3575f306a DE-627 ger DE-627 rakwb eng TP1-1185 Wenfeng Zhang verfasserin aut Diagnosing a Strong-Fault Model by Conflict and Consistency 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology Qi Zhao verfasserin aut Hongbo Zhao verfasserin aut Gan Zhou verfasserin aut Wenquan Feng verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1016 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1016 https://doi.org/10.3390/s18041016 kostenfrei https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a kostenfrei http://www.mdpi.com/1424-8220/18/4/1016 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1016
allfieldsSound	10.3390/s18041016 doi (DE-627)DOAJ08539758X (DE-599)DOAJ57d8d4998ba54f749dc47dc3575f306a DE-627 ger DE-627 rakwb eng TP1-1185 Wenfeng Zhang verfasserin aut Diagnosing a Strong-Fault Model by Conflict and Consistency 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology Qi Zhao verfasserin aut Hongbo Zhao verfasserin aut Gan Zhou verfasserin aut Wenquan Feng verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1016 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1016 https://doi.org/10.3390/s18041016 kostenfrei https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a kostenfrei http://www.mdpi.com/1424-8220/18/4/1016 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 4, p 1016
language	English
source	In Sensors 18(2018), 4, p 1016 volume:18 year:2018 number:4, p 1016
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topic_facet	fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model Chemical technology
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authorswithroles_txt_mv	Wenfeng Zhang @@aut@@ Qi Zhao @@aut@@ Hongbo Zhao @@aut@@ Gan Zhou @@aut@@ Wenquan Feng @@aut@@
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callnumber-first	T - Technology
author	Wenfeng Zhang
spellingShingle	Wenfeng Zhang misc TP1-1185 misc fault diagnosis misc model-based diagnosis misc truth maintenance system misc conflict directed A* misc a strong-fault model misc Chemical technology Diagnosing a Strong-Fault Model by Conflict and Consistency
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topic_title	TP1-1185 Diagnosing a Strong-Fault Model by Conflict and Consistency fault diagnosis model-based diagnosis truth maintenance system conflict directed A* a strong-fault model
topic	misc TP1-1185 misc fault diagnosis misc model-based diagnosis misc truth maintenance system misc conflict directed A* misc a strong-fault model misc Chemical technology
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title	Diagnosing a Strong-Fault Model by Conflict and Consistency
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title_auth	Diagnosing a Strong-Fault Model by Conflict and Consistency
abstract	The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods.
abstractGer	The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods.
abstract_unstemmed	The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods.
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title_short	Diagnosing a Strong-Fault Model by Conflict and Consistency
url	https://doi.org/10.3390/s18041016 https://doaj.org/article/57d8d4998ba54f749dc47dc3575f306a http://www.mdpi.com/1424-8220/18/4/1016 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T14:30:32.083Z
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