Simple method of failure detection of rotary machines

• Autorzy: Bielecki Andrzej , Bielecka Marzena , Jabłoński Adam , Staszewski Wojciech

Identyfikatory

DOI

10.29354/diag/142862

Warianty tytułu

PL

Prosta metoda wykrywania usterek w maszynach wirnikowych

• Języki publikacji: EN

Abstrakty

EN

In the paper a simple unsupervised monitoring method of rotary machines is proposed. The method consists of three stages - multi-reference preliminary analysis of the vibration signals, auto-reference preliminary analysis and probabilistic analysis of the signals. The method was tested by using signals from eight simulated machines. The efficiency of the method has been positively verified.

PL

W niniejszej publikacji prosta proponujemy prostą metodę nienadzorowanego monitoringu maszyn wirnikowych. Metoda jest trzystopniowa - wieloreferencyjna wstępna analiza sygnałów drganiowych, autoreferencyjna wstępna analiza oraz probabilistyczna analiza sygnałów. Metoda została przetestowana na ośmiu symulowanych maszynach. Skuteczność metody została pozytywnie zweryfikowana.

Słowa kluczowe

EN

rotary machine; intelligent monitoring; early failure detection

PL

maszyna wirnikowa; monitoring inteligentny; wczesne wykrywanie usterek

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 4
• Strony: 17--23
• Opis fizyczny: Bibliogr. 15 poz., wykr.

Twórcy

autor

Bielecki Andrzej

• Institute of Computer Science, Faculty of Exact and Natural Sciences, Pedagogical University in Kraków, Podchorążych 2, 30-084 Kraków, Poland

autor

Bielecka Marzena

• Chair of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland

autor

Jabłoński Adam

• Chair of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland

autor

Staszewski Wojciech

• Chair of Robotics and Mechatronics, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland

Bibliografia

• 1. Aggarwal C. Outlier analysis, in: Data Mining, Springer 2015; 237-263.
• 2. Bielecki A, Wójcik M. Hybrid system of ART and RBF neural networks for online clustering. Applied Soft Computing. 2017;58:1-10. https://doi.org/10.1016/j.asoc.2017.04.012.
• 3. Bielecki A, Wojcik M. Hybrid AI system based on ART neural network and mixture of Gausians modules with application to intelligent monitoring of the wind turbine. Applied Soft Computing 2021; 108:107400. https://doi.org/10.1016/j.asoc.2021.107400.
• 4. Bielecki A, Barszcz T, Wójcik M, Bielecka M. Hybrid system of ART and RBF neural networks for classification of vibration signals and operational states of wind. Lecture Notes in Artificial Intelligence. 2014;8467:3-11. https://doi.org/10.1007/978-3-319-07173-2_1.
• 5. Dao PB, Staszewski WJ, Barszcz T, Uhl T. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA system. Renevable Energy 2018; 116: 107-122. https://doi.org/10.1016/j.renene.2017.06.089.
• 6. Dinardo G, Fabbiano L, Vacca G. A smart and intuitive machine condition monitoring in the Industry 4.0 scenario. Measurement 2018; 26. https://doi.org/10.1016/j.measurement.2018.05.041.
• 7. Gan M, Wang C. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems and Signal Processing. 2016;72-73:92-104. https://doi.org/10.1016/j.ymssp.2015.11.014.
• 8. Górski J, Jabłoński A, Heesh M, Dziendzikowski M, Dworakowski Z. Comparison of novelty detection methods for detection of various rotary machinery faults. Sensors. 2021;21(10):3536; https://doi.org/10.3390/s21103536.
• 9. Jabłoński A. Condition Monitoring Algorithms in MATLAB, Springer 2021.
• 10. Randall RB. Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications. John Wiley and Sons, Ltd., 2nd edition 2021.
• 11. Rogalewicz M, Sika R. Methodologies of knowledge discovery from data and data mining methods in mechanical engineering. Management and Production Engineering Review 2016; 7(4): 97-108. https://doi.org/10.1515/mper-2016-0040.
• 12. Urbanek J, Barszcz T, Jabłoński A. Application of angular-temporal spectrum to exploratory analysis of generalized angular-temporal deterministic signals. Applied Acoustics 2016; 109: 27-36. https://doi.org/10.1016/j.apacoust.2016.03.004.
• 13. Urbanek J, Barszcz T, Strączkiewicz M, Jabłoński A. Normalization of vibration signals generated under highly varying speed and load with application to signal separation. Mechanical Systems and Signal Processing 2017;82:13-31. https://doi.org/10.1016/j.ymssp.2016.04.017.
• 14. Wodecki J, Michalak A, Zimroz R, Wyłomańska A. Separation of multiple local-damage-related components from vibration data using Nonnegative Matrix Factorization and multichannel data fusion. Mechanical Systems and Signal Processing 2020; 145:106954. https://doi.org/10.1016/j.ymssp.2020.106954.
• 15. Wodecki J, Stefaniak P, Obuchowski J, Wyłomańska A, Zimroz R. Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection. Journal of Vibroengineering. 2016; 18:2167-2175. https://doi.org/10.21595/jve.2016.17114.

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).