Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances eve...
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Gespeichert in:

Autor*in:	Sofia Fernandes [verfasserIn] Mário Antunes [verfasserIn] Ana Rita Santiago [verfasserIn] João Paulo Barraca [verfasserIn] Diogo Gomes [verfasserIn] Rui L. Aguiar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	big data applications big data services infrastructure data processing data analysis predictive maintenance

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 11(2020), 4, p 208
Übergeordnetes Werk:	volume:11 ; year:2020 ; number:4, p 208

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info11040208

Katalog-ID:	DOAJ044672454

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spelling	10.3390/info11040208 doi (DE-627)DOAJ044672454 (DE-599)DOAJ40e3889514e64f52bb594ffc88a1d508 DE-627 ger DE-627 rakwb eng T58.5-58.64 Sofia Fernandes verfasserin aut Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. big data applications big data services infrastructure data processing data analysis predictive maintenance Information technology Mário Antunes verfasserin aut Ana Rita Santiago verfasserin aut João Paulo Barraca verfasserin aut Diogo Gomes verfasserin aut Rui L. Aguiar verfasserin aut In Information MDPI AG, 2010 11(2020), 4, p 208 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2020 number:4, p 208 https://doi.org/10.3390/info11040208 kostenfrei https://doaj.org/article/40e3889514e64f52bb594ffc88a1d508 kostenfrei https://www.mdpi.com/2078-2489/11/4/208 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 4, p 208
allfields_unstemmed	10.3390/info11040208 doi (DE-627)DOAJ044672454 (DE-599)DOAJ40e3889514e64f52bb594ffc88a1d508 DE-627 ger DE-627 rakwb eng T58.5-58.64 Sofia Fernandes verfasserin aut Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. big data applications big data services infrastructure data processing data analysis predictive maintenance Information technology Mário Antunes verfasserin aut Ana Rita Santiago verfasserin aut João Paulo Barraca verfasserin aut Diogo Gomes verfasserin aut Rui L. Aguiar verfasserin aut In Information MDPI AG, 2010 11(2020), 4, p 208 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2020 number:4, p 208 https://doi.org/10.3390/info11040208 kostenfrei https://doaj.org/article/40e3889514e64f52bb594ffc88a1d508 kostenfrei https://www.mdpi.com/2078-2489/11/4/208 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 4, p 208
allfieldsGer	10.3390/info11040208 doi (DE-627)DOAJ044672454 (DE-599)DOAJ40e3889514e64f52bb594ffc88a1d508 DE-627 ger DE-627 rakwb eng T58.5-58.64 Sofia Fernandes verfasserin aut Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. big data applications big data services infrastructure data processing data analysis predictive maintenance Information technology Mário Antunes verfasserin aut Ana Rita Santiago verfasserin aut João Paulo Barraca verfasserin aut Diogo Gomes verfasserin aut Rui L. Aguiar verfasserin aut In Information MDPI AG, 2010 11(2020), 4, p 208 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2020 number:4, p 208 https://doi.org/10.3390/info11040208 kostenfrei https://doaj.org/article/40e3889514e64f52bb594ffc88a1d508 kostenfrei https://www.mdpi.com/2078-2489/11/4/208 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 4, p 208
allfieldsSound	10.3390/info11040208 doi (DE-627)DOAJ044672454 (DE-599)DOAJ40e3889514e64f52bb594ffc88a1d508 DE-627 ger DE-627 rakwb eng T58.5-58.64 Sofia Fernandes verfasserin aut Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. big data applications big data services infrastructure data processing data analysis predictive maintenance Information technology Mário Antunes verfasserin aut Ana Rita Santiago verfasserin aut João Paulo Barraca verfasserin aut Diogo Gomes verfasserin aut Rui L. Aguiar verfasserin aut In Information MDPI AG, 2010 11(2020), 4, p 208 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:11 year:2020 number:4, p 208 https://doi.org/10.3390/info11040208 kostenfrei https://doaj.org/article/40e3889514e64f52bb594ffc88a1d508 kostenfrei https://www.mdpi.com/2078-2489/11/4/208 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 4, p 208
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topic_facet	big data applications big data services infrastructure data processing data analysis predictive maintenance Information technology
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authorswithroles_txt_mv	Sofia Fernandes @@aut@@ Mário Antunes @@aut@@ Ana Rita Santiago @@aut@@ João Paulo Barraca @@aut@@ Diogo Gomes @@aut@@ Rui L. Aguiar @@aut@@
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callnumber-first	T - Technology
author	Sofia Fernandes
spellingShingle	Sofia Fernandes misc T58.5-58.64 misc big data applications misc big data services misc infrastructure misc data processing misc data analysis misc predictive maintenance misc Information technology Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
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topic_title	T58.5-58.64 Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance big data applications big data services infrastructure data processing data analysis predictive maintenance
topic	misc T58.5-58.64 misc big data applications misc big data services misc infrastructure misc data processing misc data analysis misc predictive maintenance misc Information technology
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title	Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
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title_sort	forecasting appliances failures: a machine-learning approach to predictive maintenance
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title_auth	Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
abstract	Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.
abstractGer	Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.
abstract_unstemmed	Heating appliances consume approximately <inline-formula< <math display="inline"< <semantics< <mrow< <mn<48</mn< <mo<%</mo< </mrow< </semantics< </math< </inline-formula< of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.
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title_short	Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
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