Fault diagnosis of multistage manufacturing systems based on rough set approach

Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xie, Nan [verfasserIn] Chen, Lin Li, Aiping

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Multistage manufacturing system Diagnosability Rough set theory Knowledge Statistical process control

Anmerkung:	© Springer-Verlag London Limited 2009

Übergeordnetes Werk:	Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247
Übergeordnetes Werk:	volume:48 ; year:2009 ; number:9-12 ; day:13 ; month:12 ; pages:1239-1247

Links:	Volltext

DOI / URN:	10.1007/s00170-009-2324-0

Katalog-ID:	SPR001607030

Internformat


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520			\|a Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method.
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allfields	10.1007/s00170-009-2324-0 doi (DE-627)SPR001607030 (SPR)s00170-009-2324-0-e DE-627 ger DE-627 rakwb eng Xie, Nan verfasserin aut Fault diagnosis of multistage manufacturing systems based on rough set approach 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Limited 2009 Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213 Chen, Lin aut Li, Aiping aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247 https://dx.doi.org/10.1007/s00170-009-2324-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2009 9-12 13 12 1239-1247
spelling	10.1007/s00170-009-2324-0 doi (DE-627)SPR001607030 (SPR)s00170-009-2324-0-e DE-627 ger DE-627 rakwb eng Xie, Nan verfasserin aut Fault diagnosis of multistage manufacturing systems based on rough set approach 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Limited 2009 Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213 Chen, Lin aut Li, Aiping aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247 https://dx.doi.org/10.1007/s00170-009-2324-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2009 9-12 13 12 1239-1247
allfields_unstemmed	10.1007/s00170-009-2324-0 doi (DE-627)SPR001607030 (SPR)s00170-009-2324-0-e DE-627 ger DE-627 rakwb eng Xie, Nan verfasserin aut Fault diagnosis of multistage manufacturing systems based on rough set approach 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Limited 2009 Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213 Chen, Lin aut Li, Aiping aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247 https://dx.doi.org/10.1007/s00170-009-2324-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2009 9-12 13 12 1239-1247
allfieldsGer	10.1007/s00170-009-2324-0 doi (DE-627)SPR001607030 (SPR)s00170-009-2324-0-e DE-627 ger DE-627 rakwb eng Xie, Nan verfasserin aut Fault diagnosis of multistage manufacturing systems based on rough set approach 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Limited 2009 Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213 Chen, Lin aut Li, Aiping aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247 https://dx.doi.org/10.1007/s00170-009-2324-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2009 9-12 13 12 1239-1247
allfieldsSound	10.1007/s00170-009-2324-0 doi (DE-627)SPR001607030 (SPR)s00170-009-2324-0-e DE-627 ger DE-627 rakwb eng Xie, Nan verfasserin aut Fault diagnosis of multistage manufacturing systems based on rough set approach 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Limited 2009 Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213 Chen, Lin aut Li, Aiping aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247 https://dx.doi.org/10.1007/s00170-009-2324-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2009 9-12 13 12 1239-1247
language	English
source	Enthalten in The international journal of advanced manufacturing technology 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247
sourceStr	Enthalten in The international journal of advanced manufacturing technology 48(2009), 9-12 vom: 13. Dez., Seite 1239-1247 volume:48 year:2009 number:9-12 day:13 month:12 pages:1239-1247
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multistage manufacturing system Diagnosability Rough set theory Knowledge Statistical process control
isfreeaccess_bool	false
container_title	The international journal of advanced manufacturing technology
authorswithroles_txt_mv	Xie, Nan @@aut@@ Chen, Lin @@aut@@ Li, Aiping @@aut@@
publishDateDaySort_date	2009-12-13T00:00:00Z
hierarchy_top_id	270127712
id	SPR001607030
language_de	englisch
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author	Xie, Nan
spellingShingle	Xie, Nan misc Multistage manufacturing system misc Diagnosability misc Rough set theory misc Knowledge misc Statistical process control Fault diagnosis of multistage manufacturing systems based on rough set approach
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topic_title	Fault diagnosis of multistage manufacturing systems based on rough set approach Multistage manufacturing system (dpeaa)DE-He213 Diagnosability (dpeaa)DE-He213 Rough set theory (dpeaa)DE-He213 Knowledge (dpeaa)DE-He213 Statistical process control (dpeaa)DE-He213
topic	misc Multistage manufacturing system misc Diagnosability misc Rough set theory misc Knowledge misc Statistical process control
topic_unstemmed	misc Multistage manufacturing system misc Diagnosability misc Rough set theory misc Knowledge misc Statistical process control
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title	Fault diagnosis of multistage manufacturing systems based on rough set approach
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title_full	Fault diagnosis of multistage manufacturing systems based on rough set approach
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doi_str_mv	10.1007/s00170-009-2324-0
title_sort	fault diagnosis of multistage manufacturing systems based on rough set approach
title_auth	Fault diagnosis of multistage manufacturing systems based on rough set approach
abstract	Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. © Springer-Verlag London Limited 2009
abstractGer	Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. © Springer-Verlag London Limited 2009
abstract_unstemmed	Abstract Multistage manufacturing systems (MMS) have been investigated extensively. However, quality and dimensional problems are still one of the most important research topics, especially rapid diagnosis of dimensional failures is of critical concern. Due to the knowledge and experience intension nature of fault diagnosis, the diagnostic results depend on the preference of the decision makers on the hidden relations between possible faults and presented symptoms. In this paper, a rough set-based fault diagnosis method is proposed, and a rapid fault diagnosis system with rough set is developed. The novel approach uses rough sets theory as a knowledge extraction tool to deal with the data that are obtained from both sensors and statistical process control charts and then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by experts in the field. By means of knowledge acquisition, the machining process failures in MMS can then be identified. A practical system is presented to illustrate the efficiency and effectivity of our method. © Springer-Verlag London Limited 2009
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container_issue	9-12
title_short	Fault diagnosis of multistage manufacturing systems based on rough set approach
url	https://dx.doi.org/10.1007/s00170-009-2324-0
remote_bool	true
author2	Chen, Lin Li, Aiping
author2Str	Chen, Lin Li, Aiping
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doi_str	10.1007/s00170-009-2324-0
up_date	2024-07-03T23:31:38.951Z
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Fault diagnosis of multistage manufacturing systems based on rough set approach

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