| Autor: |
Biju VG; Institute of Digital Engineering, Technical University of Applied Sciences Wuerzburg-Schweinfurt, 97421 Schweinfurt, Germany., Schmitt AM; Institute of Digital Engineering, Technical University of Applied Sciences Wuerzburg-Schweinfurt, 97421 Schweinfurt, Germany., Engelmann B; Institute of Digital Engineering, Technical University of Applied Sciences Wuerzburg-Schweinfurt, 97421 Schweinfurt, Germany. |
| Jazyk: |
angličtina |
| Zdroj: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Jan 05; Vol. 24 (2). Date of Electronic Publication: 2024 Jan 05. |
| DOI: |
10.3390/s24020330 |
| Abstrakt: |
The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA). |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
