Detection of ICE states from mechanical vibrations using entropy measurements and machine learning algorithms
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Universidad Tecnológica de Pereira
Universidad Tecnológica de Pereria
Héctor Fabio Quintero   

Universidad Tecnológica de Pereira
Submission date: 2020-07-31
Final revision date: 2020-11-07
Acceptance date: 2020-11-18
Online publication date: 2020-11-19
Publication date: 2020-11-19
Diagnostyka 2020;21(4):87–94
Entropy measurements are an accessible tool to perform irregularity and uncertainty measurements present in time series. Particularly in the area of signal processing, Multiscalar Permutation Entropy (MPE) is presented as a characterization methodology capable of measuring randomness and non-linear dynamics present in non-stationary signals, such as mechanical vibrations. In this article, we present a robust methodology based on MPE for detection of Internal Combustion Engine (ICE) states. The MPE is combined with the technique of visualization and feature selection Principal Component Analysis (PCA) and the supervised classifier of the Nearest K Neighbors (KNN). The proposed methodology is validated by comparing accuracy and computation time with others presented in the literature. The results allow to appreciate a high effectiveness in the detection of failures in bearings with a low computational consumption.
The authors thank to Ministerio de Ciencias y Tecnología of Colombia for supporting the project entitled: “Desarrollo de un sistema de monitoreo para el análisis energético y de condición de emisiones en motores de combustión interna diésel con base en técnicas no destructivas” with code 1110-776-57801, through which the research described in this article was developed.
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