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: | , , , |
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| Lenguaje: | English |
| Publicado: |
Universidad de Tarapacá.
2016
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052016000400004 |
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| Sumario: | 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. |
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