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RESEARCH PAPER
Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine
 
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1
Department of Mechanics and Vibroacoustics, AGH University of Science and Technology, Poland
 
2
Department of Quantitative Methods in Management, Lublin University of Technology, Poland
 
 
Submission date: 2023-03-08
 
 
Final revision date: 2023-04-05
 
 
Acceptance date: 2023-06-11
 
 
Online publication date: 2023-06-15
 
 
Corresponding author
Paweł Pawlik   

Department of Mechanics and Vibroacoustics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Kraków, Poland
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(3):168109
 
HIGHLIGHTS
  • It is possible to train a neural network with only data from an undamaged machine.
  • The order spectrum of the novel parameter rDPNS is proposed.
  • The new method application for diagnosing unbalance and misalignment was analysed.
  • Proposed architecture is resilient to overfitting without drop-outs and bagging.
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ABSTRACT
The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method.
ACKNOWLEDGEMENTS
This work was supported by the Polish Ministry of Science and Higher Education [grant number 16.16.130.942].
 
CITATIONS (4):
1.
Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration
Abdel LOURARI, Abdenour SOUALHI, Tarak BENKEDJOUH
 
2.
Neural net monitoring of signals parameters during the induction motors run-out
Oleg Andreev, Valentin Grigoriev, Grigoriy Malinin, Alexander Slavutskiy, Shahriyor Sadullozoda, Ramazona Abdullozoda
Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)
 
3.
Advancing bearing fault diagnosis under variable working conditions: a CEEMDAN-SBS approach with vibro-electric signal integration
Abdel wahhab Lourari, Abdenour Soualhi, Tarak Benkedjouh
The International Journal of Advanced Manufacturing Technology
 
4.
Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings
Michał Styła, Edward Kozłowski, Paweł Tchórzewski, Dominik Gnaś, Przemysław Adamkiewicz, Jan Laskowski, Sylwia Skrzypek-Ahmed, Arkadiusz Małek, Dariusz Kasperek
Energies
 
eISSN:2956-3860
ISSN:1507-2711
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