FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors. Cabal-Yepez, E., Valtierra-Rodriguez, M., Romero-Troncoso, R. J., Garcia-Perez, A., Osornio-Rios, R. A., Miranda-Vidales, H., & Alvarez-Salas, R. Mechanical Systems and Signal Processing, 30:123–130, July, 2012. ![FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time.
@article{cabal-yepez_fpga-based_2012,
title = {{FPGA}-based entropy neural processor for online detection of multiple combined faults on induction motors},
volume = {30},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327012000222},
doi = {10.1016/j.ymssp.2012.01.021},
abstract = {For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time.},
language = {en},
urldate = {2021-09-30},
journal = {Mechanical Systems and Signal Processing},
author = {Cabal-Yepez, E. and Valtierra-Rodriguez, M. and Romero-Troncoso, R. J. and Garcia-Perez, A. and Osornio-Rios, R. A. and Miranda-Vidales, H. and Alvarez-Salas, R.},
month = jul,
year = {2012},
keywords = {3-axis vibration signals, Artificial neural networks, Field programmable gate array, Induction motors, Information entropy, Multiple combined faults},
pages = {123--130},
}
Downloads: 0
{"_id":"yARCo4dzDjm8bEiaC","bibbaseid":"cabalyepez-valtierrarodriguez-romerotroncoso-garciaperez-osorniorios-mirandavidales-alvarezsalas-fpgabasedentropyneuralprocessorforonlinedetectionofmultiplecombinedfaultsoninductionmotors-2012","author_short":["Cabal-Yepez, E.","Valtierra-Rodriguez, M.","Romero-Troncoso, R. J.","Garcia-Perez, A.","Osornio-Rios, R. A.","Miranda-Vidales, H.","Alvarez-Salas, R."],"bibdata":{"bibtype":"article","type":"article","title":"FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors","volume":"30","issn":"0888-3270","url":"https://www.sciencedirect.com/science/article/pii/S0888327012000222","doi":"10.1016/j.ymssp.2012.01.021","abstract":"For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time.","language":"en","urldate":"2021-09-30","journal":"Mechanical Systems and Signal Processing","author":[{"propositions":[],"lastnames":["Cabal-Yepez"],"firstnames":["E."],"suffixes":[]},{"propositions":[],"lastnames":["Valtierra-Rodriguez"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Romero-Troncoso"],"firstnames":["R.","J."],"suffixes":[]},{"propositions":[],"lastnames":["Garcia-Perez"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Osornio-Rios"],"firstnames":["R.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Miranda-Vidales"],"firstnames":["H."],"suffixes":[]},{"propositions":[],"lastnames":["Alvarez-Salas"],"firstnames":["R."],"suffixes":[]}],"month":"July","year":"2012","keywords":"3-axis vibration signals, Artificial neural networks, Field programmable gate array, Induction motors, Information entropy, Multiple combined faults","pages":"123–130","bibtex":"@article{cabal-yepez_fpga-based_2012,\n\ttitle = {{FPGA}-based entropy neural processor for online detection of multiple combined faults on induction motors},\n\tvolume = {30},\n\tissn = {0888-3270},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0888327012000222},\n\tdoi = {10.1016/j.ymssp.2012.01.021},\n\tabstract = {For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time.},\n\tlanguage = {en},\n\turldate = {2021-09-30},\n\tjournal = {Mechanical Systems and Signal Processing},\n\tauthor = {Cabal-Yepez, E. and Valtierra-Rodriguez, M. and Romero-Troncoso, R. J. and Garcia-Perez, A. and Osornio-Rios, R. A. and Miranda-Vidales, H. and Alvarez-Salas, R.},\n\tmonth = jul,\n\tyear = {2012},\n\tkeywords = {3-axis vibration signals, Artificial neural networks, Field programmable gate array, Induction motors, Information entropy, Multiple combined faults},\n\tpages = {123--130},\n}\n\n\n\n","author_short":["Cabal-Yepez, E.","Valtierra-Rodriguez, M.","Romero-Troncoso, R. J.","Garcia-Perez, A.","Osornio-Rios, R. A.","Miranda-Vidales, H.","Alvarez-Salas, R."],"key":"cabal-yepez_fpga-based_2012","id":"cabal-yepez_fpga-based_2012","bibbaseid":"cabalyepez-valtierrarodriguez-romerotroncoso-garciaperez-osorniorios-mirandavidales-alvarezsalas-fpgabasedentropyneuralprocessorforonlinedetectionofmultiplecombinedfaultsoninductionmotors-2012","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0888327012000222"},"keyword":["3-axis vibration signals","Artificial neural networks","Field programmable gate array","Induction motors","Information entropy","Multiple combined faults"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["3-axis vibration signals","artificial neural networks","field programmable gate array","induction motors","information entropy","multiple combined faults"],"search_terms":["fpga","based","entropy","neural","processor","online","detection","multiple","combined","faults","induction","motors","cabal-yepez","valtierra-rodriguez","romero-troncoso","garcia-perez","osornio-rios","miranda-vidales","alvarez-salas"],"title":"FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors","year":2012}