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- W4366147050 abstract "Monitoring the health of lubricant oil used in a machine is one of the most effective ways to predict the machine condition. A non-invasive non-destructive monitoring approach may be used for a multiparametric causal analysis of the oil state. However, the analytical solution for the multiple contaminant determination in the oil is extremely challenging, which can be solved using a machine learning (ML) based classification approach. To train such an ML model needs a large number of datasets that is difficult to obtain experimentally. A computational model resembling the experimental system may be useful in this context. In this paper, a computational model replicating a non-invasive technique, namely, photoacoustic (PA) sensing to investigate lubricant oil is explored through a set of simulations followed by experimental validation and machine learning model-based data analysis. The effect of the two dominant causes of lubricant oil degradation, namely, soot particle and water has been investigated through simulation and experiment; and the ML analysis has been executed to classify the data into four classes depicting four states of the machine health." @default.
- W4366147050 created "2023-04-19" @default.
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- W4366147050 date "2023-01-23" @default.
- W4366147050 modified "2023-09-25" @default.
- W4366147050 title "Monte Carlo method based model for augmenting data towards lubricant oil state analysis in heavy machine industry" @default.
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- W4366147050 doi "https://doi.org/10.1109/apscon56343.2023.10101307" @default.
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