Prediction of loading spectra under diverse operating conditions by a localised basis function neural network
To estimate the durability and reliability of a structural component its design loading spectrum must be defined. This spectrum is obtained by combining the loading spectra that correspond to all possible operating conditions. It can happen, however, that some of the loading spectra that should be u...
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| Veröffentlicht in: | International journal of fatigue 2005-05, Vol.27 (5), p.555-568 |
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| container_title | International journal of fatigue |
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| creator | Klemenc, Jernej Fajdiga, Matija |
| description | To estimate the durability and reliability of a structural component its design loading spectrum must be defined. This spectrum is obtained by combining the loading spectra that correspond to all possible operating conditions. It can happen, however, that some of the loading spectra that should be used for building the design loading spectrum are not measured as a result of cost and time limitations. But if a relationship between the operating conditions and the corresponding loading spectra was known, it would be possible to predict a loading spectrum for an arbitrary combination of operating conditions. In this paper, we present a new approach that makes it possible to empirically model the relationship between the factors of the operating conditions and the corresponding loading spectra. This approach is based on a localised basis function neural network, and we have applied it on simulated and measured load states. The presented examples demonstrate that the new approach is suitable for predicting loading spectra for those combinations of operating conditions for which the load states were not measured. |
| doi_str_mv | 10.1016/j.ijfatigue.2004.09.005 |
| format | Article |
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This spectrum is obtained by combining the loading spectra that correspond to all possible operating conditions. It can happen, however, that some of the loading spectra that should be used for building the design loading spectrum are not measured as a result of cost and time limitations. But if a relationship between the operating conditions and the corresponding loading spectra was known, it would be possible to predict a loading spectrum for an arbitrary combination of operating conditions. In this paper, we present a new approach that makes it possible to empirically model the relationship between the factors of the operating conditions and the corresponding loading spectra. This approach is based on a localised basis function neural network, and we have applied it on simulated and measured load states. 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This spectrum is obtained by combining the loading spectra that correspond to all possible operating conditions. It can happen, however, that some of the loading spectra that should be used for building the design loading spectrum are not measured as a result of cost and time limitations. But if a relationship between the operating conditions and the corresponding loading spectra was known, it would be possible to predict a loading spectrum for an arbitrary combination of operating conditions. In this paper, we present a new approach that makes it possible to empirically model the relationship between the factors of the operating conditions and the corresponding loading spectra. This approach is based on a localised basis function neural network, and we have applied it on simulated and measured load states. The presented examples demonstrate that the new approach is suitable for predicting loading spectra for those combinations of operating conditions for which the load states were not measured.</description><subject>Applied sciences</subject><subject>Exact sciences and technology</subject><subject>Fatigue</subject><subject>Loading spectrum</subject><subject>Localised basis function neural network</subject><subject>Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology</subject><subject>Metals. Metallurgy</subject><subject>Multivariate Gaussian function</subject><subject>Operating conditions</subject><subject>Rainflow method</subject><issn>0142-1123</issn><issn>1879-3452</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNqFkEFv1DAQhS0EEkvhN-AL3BLGsZ2sj1UFBakSHOBsOfa48pK1F09S1H-PV1vBkdMc5r158z7G3groBYjxw6FPh-jWdL9hPwCoHkwPoJ-xndhPppNKD8_ZDoQaOiEG-ZK9IjoAgIFJ79jxW8WQ_JpK5iXypbiQ8j2nE_q1Or7lgJWH9ICVkJcT1pbU9r7kkM4m4vMjd83n3ZIIA58dJeJxy5ebGbfqljbW36X-fM1eRLcQvnmaV-zHp4_fbz53d19vv9xc33VeTuPaOR0n1T70zqBRwQwQBYZZOaX2ynsDRsZRjwFng2OUZpglyFnIPUYIg1fyir2_3D3V8mtDWu0xkcdlcRnLRnbYa6OlhiacLkJfC1HFaE81HV19tALsGa892L947RmvBWMb3uZ89xThqHWP1WWf6J-9_aeMNk13fdFh6_uQsFryCbNv1GtjbENJ_836A9x5l5M</recordid><startdate>20050501</startdate><enddate>20050501</enddate><creator>Klemenc, Jernej</creator><creator>Fajdiga, Matija</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20050501</creationdate><title>Prediction of loading spectra under diverse operating conditions by a localised basis function neural network</title><author>Klemenc, Jernej ; Fajdiga, Matija</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-a5f74009ca9e94d920f1edb4a4484cc9093f656deb9e6f392b303b138ef0d2c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Exact sciences and technology</topic><topic>Fatigue</topic><topic>Loading spectrum</topic><topic>Localised basis function neural network</topic><topic>Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology</topic><topic>Metals. Metallurgy</topic><topic>Multivariate Gaussian function</topic><topic>Operating conditions</topic><topic>Rainflow method</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Klemenc, Jernej</creatorcontrib><creatorcontrib>Fajdiga, Matija</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of fatigue</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Klemenc, Jernej</au><au>Fajdiga, Matija</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of loading spectra under diverse operating conditions by a localised basis function neural network</atitle><jtitle>International journal of fatigue</jtitle><date>2005-05-01</date><risdate>2005</risdate><volume>27</volume><issue>5</issue><spage>555</spage><epage>568</epage><pages>555-568</pages><issn>0142-1123</issn><eissn>1879-3452</eissn><coden>IJFADB</coden><abstract>To estimate the durability and reliability of a structural component its design loading spectrum must be defined. This spectrum is obtained by combining the loading spectra that correspond to all possible operating conditions. It can happen, however, that some of the loading spectra that should be used for building the design loading spectrum are not measured as a result of cost and time limitations. But if a relationship between the operating conditions and the corresponding loading spectra was known, it would be possible to predict a loading spectrum for an arbitrary combination of operating conditions. In this paper, we present a new approach that makes it possible to empirically model the relationship between the factors of the operating conditions and the corresponding loading spectra. This approach is based on a localised basis function neural network, and we have applied it on simulated and measured load states. 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| ispartof | International journal of fatigue, 2005-05, Vol.27 (5), p.555-568 |
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| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Applied sciences Exact sciences and technology Fatigue Loading spectrum Localised basis function neural network Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology Metals. Metallurgy Multivariate Gaussian function Operating conditions Rainflow method |
| title | Prediction of loading spectra under diverse operating conditions by a localised basis function neural network |
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