Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains

Preventive maintenance is a philosophy for assets management that aims to maximize operation through routine inspections with increasing frequency when no abnormalities are exhibit. This leads to an increase in the probability of failure due to the repetitive intervention and the inherent human erro...

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Autores principales:	Holguín,Mauricio, Ángel Orozco,Álvaro, Holguín,Germán A, Álvarez,Mauricio
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2016
Materias:	Fault identification feature extraction Hidden Markov Chains k-means clustering Mel-Frequency Cepstral Coefficients remaining useful life
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052016000400004
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spelling	oai:scielo:S0718-330520160004000042016-11-14Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov ChainsHolguín,MauricioÁngel Orozco,ÁlvaroHolguín,Germán AÁlvarez,Mauricio Fault identification feature extraction Hidden Markov Chains k-means clustering Mel-Frequency Cepstral Coefficients remaining useful life Preventive maintenance is a philosophy for assets management that aims to maximize operation through routine inspections with increasing frequency when no abnormalities are exhibit. This leads to an increase in the probability of failure due to the repetitive intervention and the inherent human error. Recently, forecasting research, or predictive research, have been addressed in order to obtain effective maintenance strategies and evaluate and manage the residual risk in equipment susceptible to degradation. Predictive research is related to the estimation of an active's Remaining Useful Life (RUL) by predicting its health state through the progression of its degradation. This article presents the development of an automated system that identifies types of faults in bearings, using Cepstral Coefficients on the Mel scale (MFCC) as the features set for description, and Hidden Markov Chains (HMC) with discrete observations as the classification method. Here we emphasizes on the optimal selection of the states in the HMM classifier using the ROC (Receiver Operating Characteristic) curves criteria. Features are discretized using clustering by k-means. Signals in this study are vibrations signals from the bearings in electrical machinery. The two databases used here are labeled with four different operation scenarios: normal, inner ring fault, outer ring fault, and rolling element fault. One of the databases allows for differentiation in severity levels for each scenario.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.24 n.4 20162016-10-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052016000400004en10.4067/S0718-33052016000400004
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Fault identification feature extraction Hidden Markov Chains k-means clustering Mel-Frequency Cepstral Coefficients remaining useful life
spellingShingle	Fault identification feature extraction Hidden Markov Chains k-means clustering Mel-Frequency Cepstral Coefficients remaining useful life Holguín,Mauricio Ángel Orozco,Álvaro Holguín,Germán A Álvarez,Mauricio Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
description	Preventive maintenance is a philosophy for assets management that aims to maximize operation through routine inspections with increasing frequency when no abnormalities are exhibit. This leads to an increase in the probability of failure due to the repetitive intervention and the inherent human error. Recently, forecasting research, or predictive research, have been addressed in order to obtain effective maintenance strategies and evaluate and manage the residual risk in equipment susceptible to degradation. Predictive research is related to the estimation of an active's Remaining Useful Life (RUL) by predicting its health state through the progression of its degradation. This article presents the development of an automated system that identifies types of faults in bearings, using Cepstral Coefficients on the Mel scale (MFCC) as the features set for description, and Hidden Markov Chains (HMC) with discrete observations as the classification method. Here we emphasizes on the optimal selection of the states in the HMM classifier using the ROC (Receiver Operating Characteristic) curves criteria. Features are discretized using clustering by k-means. Signals in this study are vibrations signals from the bearings in electrical machinery. The two databases used here are labeled with four different operation scenarios: normal, inner ring fault, outer ring fault, and rolling element fault. One of the databases allows for differentiation in severity levels for each scenario.
author	Holguín,Mauricio Ángel Orozco,Álvaro Holguín,Germán A Álvarez,Mauricio
author_facet	Holguín,Mauricio Ángel Orozco,Álvaro Holguín,Germán A Álvarez,Mauricio
author_sort	Holguín,Mauricio
title	Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
title_short	Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
title_full	Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
title_fullStr	Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
title_full_unstemmed	Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains
title_sort	optimal state selection and tuning parameters for a degradation model in bearings using mel-frequency cepstral coefficients and hidden markov chains
publisher	Universidad de Tarapacá.
publishDate	2016
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052016000400004
work_keys_str_mv	AT holguinmauricio optimalstateselectionandtuningparametersforadegradationmodelinbearingsusingmelfrequencycepstralcoefficientsandhiddenmarkovchains AT angelorozcoalvaro optimalstateselectionandtuningparametersforadegradationmodelinbearingsusingmelfrequencycepstralcoefficientsandhiddenmarkovchains AT holguingermana optimalstateselectionandtuningparametersforadegradationmodelinbearingsusingmelfrequencycepstralcoefficientsandhiddenmarkovchains AT alvarezmauricio optimalstateselectionandtuningparametersforadegradationmodelinbearingsusingmelfrequencycepstralcoefficientsandhiddenmarkovchains
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Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains

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