Chapter Machine Learning Models for Industrial Applications
More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly det...
Na minha lista:
| Main Authors: | , , |
|---|---|
| Formato: | Online |
| Idioma: | inglês |
| Publicado em: |
InTechOpen
2021
|
| Assuntos: | |
| Acesso em linha: | ONIX_20210602_10.5772/intechopen.93043_498 |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
| _version_ | 1869513905457332224 |
|---|---|
| author | Enislay, Ramentol Tomas, Olsson Shaibal, Barua |
| author_browse | Enislay, Ramentol Shaibal, Barua Tomas, Olsson |
| author_facet | Enislay, Ramentol Tomas, Olsson Shaibal, Barua |
| author_sort | Enislay, Ramentol |
| collection | Directory of Open Access Books |
| description | More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions. |
| format | Online |
| id | doab-20.500.12854ir-70162 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-701622025-08-13T14:11:26Z Chapter Machine Learning Models for Industrial Applications Enislay, Ramentol Tomas, Olsson Shaibal, Barua machine learning, prediction, regression methods, maintenance, degradation prediction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions. 2021-06-02T10:13:42Z 2021 chapter ONIX_20210602_10.5772/intechopen.93043_498 https://library.oapen.org/handle/20.500.12657/49384 https://directory.doabooks.org/handle/20.500.12854/70162 eng open access image/jpeg image/jpeg image/jpeg n/a n/a n/a https://library.oapen.org/bitstream/20.500.12657/49384/1/72763.pdf https://library.oapen.org/bitstream/20.500.12657/49384/1/72763.pdf https://library.oapen.org/bitstream/20.500.12657/49384/1/72763.pdf InTechOpen 10.5772/intechopen.93043 10.5772/intechopen.93043 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | machine learning, prediction, regression methods, maintenance, degradation prediction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general Enislay, Ramentol Tomas, Olsson Shaibal, Barua Chapter Machine Learning Models for Industrial Applications |
| title | Chapter Machine Learning Models for Industrial Applications |
| title_full | Chapter Machine Learning Models for Industrial Applications |
| title_fullStr | Chapter Machine Learning Models for Industrial Applications |
| title_full_unstemmed | Chapter Machine Learning Models for Industrial Applications |
| title_short | Chapter Machine Learning Models for Industrial Applications |
| title_sort | chapter machine learning models for industrial applications |
| topic | machine learning, prediction, regression methods, maintenance, degradation prediction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| topic_facet | machine learning, prediction, regression methods, maintenance, degradation prediction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general |
| url | ONIX_20210602_10.5772/intechopen.93043_498 |
| work_keys_str_mv | AT enislayramentol chaptermachinelearningmodelsforindustrialapplications AT tomasolsson chaptermachinelearningmodelsforindustrialapplications AT shaibalbarua chaptermachinelearningmodelsforindustrialapplications |