- Tytuł:
- Detection of ice states from mechanical vibrations using entropy measurements and machine learning algorithms
- Autorzy:
-
Mejía, Juan C.
Quintero, Héctor F.
Echeverry-Correa, Julián D.
Romero, Carlos A. - Powiązania:
- https://bibliotekanauki.pl/articles/327980.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
entropy
vibration
dynamics
permutation
signal processing
entropia
drgania
dynamika
permutacja
przetwarzanie sygnałów - Opis:
- Entropy measurements are an accessible tool to perform irregularity and uncertainty measurements present in time series. Particularly in the area of signal processing, Multiscale 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 Principal Component Analysis (PCA) as a technique for visualization and feature selection and KNearest Neighbors (KNN) as a supervised classifier. 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 (experiment 1) and ICE states (experiment 2) with a low computational consumption.
- Źródło:
-
Diagnostyka; 2020, 21, 4; 87-94
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł