Predict industrial equipment failure with time windows and transfer learning

Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that th...
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Autor*in:	Wang, Hongzhi [verfasserIn] Lu, Wenbo Tang, Shihan Song, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Transfer learning Time Windows

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 52(2021), 3 vom: 09. Juni, Seite 2346-2358
Übergeordnetes Werk:	volume:52 ; year:2021 ; number:3 ; day:09 ; month:06 ; pages:2346-2358

Links:	Volltext

DOI / URN:	10.1007/s10489-021-02441-z

Katalog-ID:	SPR046190163

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allfields	10.1007/s10489-021-02441-z doi (DE-627)SPR046190163 (SPR)s10489-021-02441-z-e DE-627 ger DE-627 rakwb eng Wang, Hongzhi verfasserin (orcid)0000-0002-7521-2871 aut Predict industrial equipment failure with time windows and transfer learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213 Lu, Wenbo aut Tang, Shihan aut Song, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 3 vom: 09. Juni, Seite 2346-2358 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358 https://dx.doi.org/10.1007/s10489-021-02441-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2021 3 09 06 2346-2358
spelling	10.1007/s10489-021-02441-z doi (DE-627)SPR046190163 (SPR)s10489-021-02441-z-e DE-627 ger DE-627 rakwb eng Wang, Hongzhi verfasserin (orcid)0000-0002-7521-2871 aut Predict industrial equipment failure with time windows and transfer learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213 Lu, Wenbo aut Tang, Shihan aut Song, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 3 vom: 09. Juni, Seite 2346-2358 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358 https://dx.doi.org/10.1007/s10489-021-02441-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2021 3 09 06 2346-2358
allfields_unstemmed	10.1007/s10489-021-02441-z doi (DE-627)SPR046190163 (SPR)s10489-021-02441-z-e DE-627 ger DE-627 rakwb eng Wang, Hongzhi verfasserin (orcid)0000-0002-7521-2871 aut Predict industrial equipment failure with time windows and transfer learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213 Lu, Wenbo aut Tang, Shihan aut Song, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 3 vom: 09. Juni, Seite 2346-2358 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358 https://dx.doi.org/10.1007/s10489-021-02441-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2021 3 09 06 2346-2358
allfieldsGer	10.1007/s10489-021-02441-z doi (DE-627)SPR046190163 (SPR)s10489-021-02441-z-e DE-627 ger DE-627 rakwb eng Wang, Hongzhi verfasserin (orcid)0000-0002-7521-2871 aut Predict industrial equipment failure with time windows and transfer learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213 Lu, Wenbo aut Tang, Shihan aut Song, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 3 vom: 09. Juni, Seite 2346-2358 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358 https://dx.doi.org/10.1007/s10489-021-02441-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2021 3 09 06 2346-2358
allfieldsSound	10.1007/s10489-021-02441-z doi (DE-627)SPR046190163 (SPR)s10489-021-02441-z-e DE-627 ger DE-627 rakwb eng Wang, Hongzhi verfasserin (orcid)0000-0002-7521-2871 aut Predict industrial equipment failure with time windows and transfer learning 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213 Lu, Wenbo aut Tang, Shihan aut Song, Yang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2021), 3 vom: 09. Juni, Seite 2346-2358 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358 https://dx.doi.org/10.1007/s10489-021-02441-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2021 3 09 06 2346-2358
language	English
source	Enthalten in Applied intelligence 52(2021), 3 vom: 09. Juni, Seite 2346-2358 volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358
sourceStr	Enthalten in Applied intelligence 52(2021), 3 vom: 09. Juni, Seite 2346-2358 volume:52 year:2021 number:3 day:09 month:06 pages:2346-2358
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Transfer learning Time Windows
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Wang, Hongzhi @@aut@@ Lu, Wenbo @@aut@@ Tang, Shihan @@aut@@ Song, Yang @@aut@@
publishDateDaySort_date	2021-06-09T00:00:00Z
hierarchy_top_id	271180919
id	SPR046190163
language_de	englisch
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author	Wang, Hongzhi
spellingShingle	Wang, Hongzhi misc Transfer learning misc Time Windows Predict industrial equipment failure with time windows and transfer learning
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topic_title	Predict industrial equipment failure with time windows and transfer learning Transfer learning (dpeaa)DE-He213 Time Windows (dpeaa)DE-He213
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title	Predict industrial equipment failure with time windows and transfer learning
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title_full	Predict industrial equipment failure with time windows and transfer learning
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title_sort	predict industrial equipment failure with time windows and transfer learning
title_auth	Predict industrial equipment failure with time windows and transfer learning
abstract	Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	Predict industrial equipment failure with time windows and transfer learning
url	https://dx.doi.org/10.1007/s10489-021-02441-z
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up_date	2024-07-03T20:57:36.575Z
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If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transfer learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Time Windows</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lu, Wenbo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Shihan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Song, Yang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991</subfield><subfield code="g">52(2021), 3 vom: 09. 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