An Alternative Approach to Estimate Load Torque in Industrial Environment Using Neural Networks
The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimat...
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| creator | Goedtel, A. da Silva, I.N. Serni, P.J.A. Flauzino, R.A. |
| description | The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach |
| doi_str_mv | 10.1109/ICIEA.2006.257362 |
| format | Conference Proceeding |
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The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach</description><subject>Artificial neural networks</subject><subject>Differential equations</subject><subject>Electrical equipment industry</subject><subject>Induction motors</subject><subject>Industrial control</subject><subject>Neural networks</subject><subject>Shafts</subject><subject>State estimation</subject><subject>Steady-state</subject><subject>Torque measurement</subject><isbn>0780395131</isbn><isbn>9780780395138</isbn><isbn>078039514X</isbn><isbn>9780780395145</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFjsFKxDAURSMiqON8gLjJD0x9SZq2WZahOoVh3FRwV9L2VaOdtCbpiH9vQcG7uRwOXC4htwwixkDdl9uyyCMOkERcpiLhZ-Qa0gyEkix-Of8HwS7J2vt3WLIwZOkVqXNL8yGgszqYE9J8mtyo2zcaRlr4YI46IN2PuqPV6D5npMbS0nazD87ogRb2ZNxoj2gDffbGvtIDzm4RBwxfo_vwN-Si14PH9V-vSPVQVNvdZv_0WG7z_cYoCBsWM9FgJ1uGTQp9x7kUMYASPIlZH7cKMkj6JmEpyB6aTAklJHaMaS614kqsyN3vrEHEenLLb_ddx8ClSoT4AbOmU8g</recordid><startdate>200605</startdate><enddate>200605</enddate><creator>Goedtel, A.</creator><creator>da Silva, I.N.</creator><creator>Serni, P.J.A.</creator><creator>Flauzino, R.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200605</creationdate><title>An Alternative Approach to Estimate Load Torque in Industrial Environment Using Neural Networks</title><author>Goedtel, A. ; da Silva, I.N. ; Serni, P.J.A. ; Flauzino, R.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1413bed5c1eb70fd2253400932641f4c90806fb61705f0b893935ed11a25a9293</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Artificial neural networks</topic><topic>Differential equations</topic><topic>Electrical equipment industry</topic><topic>Induction motors</topic><topic>Industrial control</topic><topic>Neural networks</topic><topic>Shafts</topic><topic>State estimation</topic><topic>Steady-state</topic><topic>Torque measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Goedtel, A.</creatorcontrib><creatorcontrib>da Silva, I.N.</creatorcontrib><creatorcontrib>Serni, P.J.A.</creatorcontrib><creatorcontrib>Flauzino, R.A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Goedtel, A.</au><au>da Silva, I.N.</au><au>Serni, P.J.A.</au><au>Flauzino, R.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Alternative Approach to Estimate Load Torque in Industrial Environment Using Neural Networks</atitle><btitle>2006 1ST IEEE Conference on Industrial Electronics and Applications</btitle><stitle>ICIEA</stitle><date>2006-05</date><risdate>2006</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><isbn>0780395131</isbn><isbn>9780780395138</isbn><eisbn>078039514X</eisbn><eisbn>9780780395145</eisbn><abstract>The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as a tool for estimating the load torque applied to the induction motor shaft rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach</abstract><pub>IEEE</pub><doi>10.1109/ICIEA.2006.257362</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2006 1ST IEEE Conference on Industrial Electronics and Applications, 2006, p.1-6 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Differential equations Electrical equipment industry Induction motors Industrial control Neural networks Shafts State estimation Steady-state Torque measurement |
| title | An Alternative Approach to Estimate Load Torque in Industrial Environment Using Neural Networks |
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