Analysis of the possibility of mechanical devices supervision based on the measurement of the vibration level and SVM classifiers

• Autorzy: Schossler Ryszard , Grochowina Marcin , Leniowska Lucyna

Identyfikatory

DOI

10.21008/j.0860-6897.2022.3.16

• Języki publikacji: EN

Abstrakty

EN

This paper presents the concept of diagnosing the technical condition of mechanical devices. The test is based on a non-invasive vibration analysis technique combined with the use of artificial intelligence method. The object of the research is an electric motor for which vibrations were recorded by a vibration sensor based on four 3-axis digital accelerometers and MPU-6050 gyroscopes. The effectiveness of classification methods using the two-class and one-class classification was compared. It has been shown that the use of an incomplete pattern of the vibration model and a single-class classifier allows for effective detection of anomalies in the operation of an induction motor. Satisfactory classification efficiency was achieved, despite the limitation of the teaching set only to the information obtained during the correct operation of the device. The described method is universal and can be used to diagnose the technical condition of many different types of technical devices.

Słowa kluczowe

EN

SVM classifier; vibration pattern; gyroscope; accelerometer; one-class classifier; two-class classifier

PL

klasyfikator SVM; wzór drgań; żyroskop; akcelerometr; klasyfikator jednoklasowy; klasyfikator dwuklasowy

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2022
• Tom: Vol. 33, nr 3
• Strony: art. no. 2022316
• Opis fizyczny: Bibliogr. 11 poz., il. kolor., rys., wykr.

Twórcy

autor

Schossler Ryszard

• Uniwersytet Rzeszowski, Al Rejtana 16c, 35-959 Rzeszów

• https://orcid.org/0000-0002-2796-5406

autor

Grochowina Marcin

• Uniwersytet Rzeszowski, Al Rejtana 16c, 35-959 Rzeszów

• https://orcid.org/0000-0001-6033-0210

autor

Leniowska Lucyna

• Uniwersytet Rzeszowski, Al Rejtana 16c, 35-959 Rzeszów

• https://orcid.org/0000-0002-7994-7867

Bibliografia

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• 4. R. B. Randall, J. Antoni; Rolling element bearing diagnostics a tutorial; Mechanical systems and signal processing 2011, 25(2), 485-520.
• 5. Y.-F. Li, M. Zuo, K. Feng, Y.-J. Chen; Detection of bearing faults using a novel adaptive morphological update lifting wavelet; Chinese Journal of, Mechanical Engineering 2017, 30, 1305-1313.
• 6. D. Gil, M. Grochowina, L. Leniowska; Detecting of the rolling bearing state based on acoustic signal and K-NN classifier; IEEE SPA 2019 - Signal Processing, Algorithms, Architectures, Arrangements and Applications, Conference Proceedings, Poznań, 2019, 246-249.
• 7. V.J. Suryawanshi, A.C Pawar, S.P. Palekar, K.A. Rade; Defect detection of composite honeycomb structure by vibration analysis technique, Materials Today: Proceedings 2020, 27, 2731-2735.
• 8. X.Chiementin, B.Kilundu, J.P.Dron, P.Dehombreux, K.Debray; Effect of cascade methods on vibration defects detection, Journal of Vibration and Control 2011, 17, 567-577.
• 9. A.K.Yadav, H,Malik, S.S.Chandel; Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models; Renewable and Sustainable Energy Reviews 2014, 31, 509-519.
• 10. G.Meena, R.R.Choudhary; A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA; International Conference on Computer, Communications and Electronics (Comptelix) 2017, 553-558.
• 11. U. Dackermann, W.A. Smith, R.B. Randall; Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks; Structural Health Monitoring 2014, 13(4), 430-444. DOI:10.1177/1475921714542890

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).