An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. A...
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Autor*in:	Yaping Li [verfasserIn] Haiyan Li [verfasserIn] Zhen Chen [verfasserIn] Ying Zhu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	hidden Markov model (HMM) autocorrelation residual chart

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 15(2022), 5, p 1685
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:5, p 1685

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en15051685

Katalog-ID:	DOAJ084606169

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spelling	10.3390/en15051685 doi (DE-627)DOAJ084606169 (DE-599)DOAJca542f905152425ea5a9fa0d36d92b3a DE-627 ger DE-627 rakwb eng Yaping Li verfasserin aut An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs. hidden Markov model (HMM) autocorrelation residual chart Technology T Haiyan Li verfasserin aut Zhen Chen verfasserin aut Ying Zhu verfasserin aut In Energies MDPI AG, 2008 15(2022), 5, p 1685 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:5, p 1685 https://doi.org/10.3390/en15051685 kostenfrei https://doaj.org/article/ca542f905152425ea5a9fa0d36d92b3a kostenfrei https://www.mdpi.com/1996-1073/15/5/1685 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 5, p 1685
allfields_unstemmed	10.3390/en15051685 doi (DE-627)DOAJ084606169 (DE-599)DOAJca542f905152425ea5a9fa0d36d92b3a DE-627 ger DE-627 rakwb eng Yaping Li verfasserin aut An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs. hidden Markov model (HMM) autocorrelation residual chart Technology T Haiyan Li verfasserin aut Zhen Chen verfasserin aut Ying Zhu verfasserin aut In Energies MDPI AG, 2008 15(2022), 5, p 1685 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:5, p 1685 https://doi.org/10.3390/en15051685 kostenfrei https://doaj.org/article/ca542f905152425ea5a9fa0d36d92b3a kostenfrei https://www.mdpi.com/1996-1073/15/5/1685 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 5, p 1685
allfieldsGer	10.3390/en15051685 doi (DE-627)DOAJ084606169 (DE-599)DOAJca542f905152425ea5a9fa0d36d92b3a DE-627 ger DE-627 rakwb eng Yaping Li verfasserin aut An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs. hidden Markov model (HMM) autocorrelation residual chart Technology T Haiyan Li verfasserin aut Zhen Chen verfasserin aut Ying Zhu verfasserin aut In Energies MDPI AG, 2008 15(2022), 5, p 1685 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:5, p 1685 https://doi.org/10.3390/en15051685 kostenfrei https://doaj.org/article/ca542f905152425ea5a9fa0d36d92b3a kostenfrei https://www.mdpi.com/1996-1073/15/5/1685 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 5, p 1685
allfieldsSound	10.3390/en15051685 doi (DE-627)DOAJ084606169 (DE-599)DOAJca542f905152425ea5a9fa0d36d92b3a DE-627 ger DE-627 rakwb eng Yaping Li verfasserin aut An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs. hidden Markov model (HMM) autocorrelation residual chart Technology T Haiyan Li verfasserin aut Zhen Chen verfasserin aut Ying Zhu verfasserin aut In Energies MDPI AG, 2008 15(2022), 5, p 1685 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:15 year:2022 number:5, p 1685 https://doi.org/10.3390/en15051685 kostenfrei https://doaj.org/article/ca542f905152425ea5a9fa0d36d92b3a kostenfrei https://www.mdpi.com/1996-1073/15/5/1685 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_206 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_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 5, p 1685
language	English
source	In Energies 15(2022), 5, p 1685 volume:15 year:2022 number:5, p 1685
sourceStr	In Energies 15(2022), 5, p 1685 volume:15 year:2022 number:5, p 1685
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hidden Markov model (HMM) autocorrelation residual chart Technology T
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container_title	Energies
authorswithroles_txt_mv	Yaping Li @@aut@@ Haiyan Li @@aut@@ Zhen Chen @@aut@@ Ying Zhu @@aut@@
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author	Yaping Li
spellingShingle	Yaping Li misc hidden Markov model (HMM) misc autocorrelation misc residual chart misc Technology misc T An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations
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topic_title	An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations hidden Markov model (HMM) autocorrelation residual chart
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title	An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations
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title_sort	improved hidden markov model for monitoring the process with autocorrelated observations
title_auth	An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations
abstract	With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.
abstractGer	With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.
abstract_unstemmed	With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common. Residual charts are a good approach to monitoring the process with data autocorrelation. An improved hidden Markov model (IHMM) for the prediction of autocorrelated observations and a new expectation maximization (EM) algorithm is proposed. A residual chart based on IHMM is employed to monitor the autocorrelated process. The numerical experiment shows that, in general, IHMMs outperform both conventional hidden Markov models (HMMs) and autoregressive (AR) models in quality shift diagnosis, decreasing the cost of missing alarms. Moreover, the times taken by IHMMs for training and prediction are found to be much less than those of HMMs.
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title_short	An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations
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An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

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