Optimization of condition-based maintenance using soft computing
Abstract Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. Soft computing is...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Goyal, Deepam [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Natural Computing Applications Forum 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 28(2016), Suppl 1 vom: 08. Juni, Seite 829-844 |
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| Übergeordnetes Werk: |
volume:28 ; year:2016 ; number:Suppl 1 ; day:08 ; month:06 ; pages:829-844 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-016-2377-6 |
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OLC2025599447 |
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10.1007/s00521-016-2377-6 doi (DE-627)OLC2025599447 (DE-He213)s00521-016-2377-6-p DE-627 ger DE-627 rakwb eng 004 VZ Goyal, Deepam verfasserin aut Optimization of condition-based maintenance using soft computing 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2016 Abstract Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. Soft computing is a better alternate to predict and optimize the manufacturing processes related to physics-based models as these processes are complex and precarious. The theories of artificial neural systems, fuzzy logic, genetic algorithms, ant colony optimization, simulated annealing and particle swarm optimization are utilized by soft computing techniques to handle real-world issues that cannot be palatably handled utilizing conventional computing methods. This paper presents a state-of-the-art review on the recent developments in the use of soft computing in condition-based maintenance in manufacturing. Soft computing techniques Condition-based maintenance Manufacturing processes Optimization Pabla, B. S. aut Dhami, S. S. aut Lachhwani, Kailash aut Enthalten in Neural computing & applications Springer London, 1993 28(2016), Suppl 1 vom: 08. Juni, Seite 829-844 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:28 year:2016 number:Suppl 1 day:08 month:06 pages:829-844 https://doi.org/10.1007/s00521-016-2377-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 28 2016 Suppl 1 08 06 829-844 |
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Abstract Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. Soft computing is a better alternate to predict and optimize the manufacturing processes related to physics-based models as these processes are complex and precarious. The theories of artificial neural systems, fuzzy logic, genetic algorithms, ant colony optimization, simulated annealing and particle swarm optimization are utilized by soft computing techniques to handle real-world issues that cannot be palatably handled utilizing conventional computing methods. This paper presents a state-of-the-art review on the recent developments in the use of soft computing in condition-based maintenance in manufacturing. © The Natural Computing Applications Forum 2016 |
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Abstract Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. Soft computing is a better alternate to predict and optimize the manufacturing processes related to physics-based models as these processes are complex and precarious. The theories of artificial neural systems, fuzzy logic, genetic algorithms, ant colony optimization, simulated annealing and particle swarm optimization are utilized by soft computing techniques to handle real-world issues that cannot be palatably handled utilizing conventional computing methods. This paper presents a state-of-the-art review on the recent developments in the use of soft computing in condition-based maintenance in manufacturing. © The Natural Computing Applications Forum 2016 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025599447</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114712.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-016-2377-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025599447</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-016-2377-6-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Goyal, Deepam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimization of condition-based maintenance using soft computing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Natural Computing Applications Forum 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Due to high costs associated with conventional maintenance strategies, application of soft computing in monitoring the condition of equipment to predict the health of various components of machine tools in manufacturing processes has attracted the attention of researchers. 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