The use of a Naive Bayes Classifier in continuous monitoring systems for rotating machinery
1
Department of Mechanics and Vibroacoustics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Kraków, Poland
Submission date: 2025-03-14
Final revision date: 2025-08-21
Acceptance date: 2025-08-22
Online publication date: 2025-09-10
Publication date: 2025-09-10
Corresponding author
Bogumił Chazy
Department of Mechanics and Vibroacoustics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Kraków, Poland
Department of Mechanics and Vibroacoustics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Kraków, Poland
Diagnostyka 2025;26(3):2025313
KEYWORDS
machine learningvibroacoustic diagnosticsvariable loadmachine condition monitoringreal-time operating system
TOPICS
ABSTRACT
This paper presents a method for diagnosing rotating machines operating under varying conditions based on order analysis, Gaussian mixture models (GMMs) and Bayes inference. A classifier based on the values of the amplitudes of the order spectrum and the value of the rotational speed changing due to the variable load of the machine was constructed.
An analysis was conducted on the functionality of the method in diagnosing misalignment and unbalance of a drive system consisting of a drive motor and planetary gearbox. The drive train was subjected to a variable load of the main bucket wheel gear of a wheeled excavator at a variable oil temperature. In the diagnostic experiment, the method was shown to be highly effective in diagnosing preset system faults. The implementability of the method in embedded systems was also investigated. The method was implemented in a system with a real-time operating system and an FPGA. The method was tested in continuous monitoring mode on a laboratory bench.
FUNDING
The project was financed by the Polish Ministry of Science and Higher Education [project No. 16.16.130.942].
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