Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level

• Autorzy: Sikora Marek , Szczyrba Krzysztof , Wróbel Łukasz , Michalak Marcin

Identyfikatory

DOI

10.17531/ein.2019.2.19

Warianty tytułu

PL

Monitorowanie i utrzymanie suwnicy bramowej na podstawie bezprzewodowego systemu pomiaru i analizy poziomu drgań

• Języki publikacji: EN PL

Abstrakty

EN

The paper describes a system for monitoring and diagnosing a gantry. The main goal of the system is to acquire, visualize and monitor vibration levels of the gantry crucial elements. The system is also equipped with a computing and analytical part which enables predictive maintenance related to the vibration level assessment. The system architecture can be used in other applications too, i.e. those which require a wireless network of vibration sensors to carry out diagnostic tasks.

PL

W artykule przedstawiono system monitorowania i diagnostyki suwnicy bramowej. Głównym zadaniem systemu jest akwizycja, wizualizacja i monitorowanie poziomu drgań newralgicznych elementów suwnicy. System wyposażony jest również w część obliczeniowoanalityczną, umożliwiającą realizację zadań predykcyjnego utrzymania ruchu (ang. predictive maintenance) związanych z oceną poziomu drgań. Architektura systemu umożliwia wykorzystanie go również do innych zastosowań, w których dla realizacji zadania diagnostyki wymagana jest bezprzewodowa sieć czujników drgań.

Słowa kluczowe

EN

vibration sensor; monitoring system; maintenance; gantry; predictive maintenance; data mining; trend analysis

PL

czujnik drgań; system monitorowania; utrzymanie; suwnica; utrzymanie predykcyjne; eksploracja danych; analiza trendu

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2019
• Tom: Vol. 21, no. 2
• Strony: 341--350
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Sikora Marek

• Institute of Informatics, Silesian University of Technology ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Szczyrba Krzysztof

• Institute of Informatics, Silesian University of Technology ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Wróbel Łukasz

• Institute of Informatics, Silesian University of Technology ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Michalak Marcin

• Institute of Informatics, Silesian University of Technology ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

• 1. Antoni J. The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing 2006;20(2): 282-307, https://doi.org/10.1016/j.ymssp.2004.09.001.
• 2. Bartelmus W, Zimroz R. Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems and Signal Processing 2008; 23: 246-257, https://doi.org/10.1016/j.ymssp.2008.03.016.
• 3. Bartelmus W, Zimroz R. A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mechanical Systems and Signal Processing 2009; 23(5):1528-1534, https://doi.org/10.1016/j.ymssp.2009.01.014.
• 4. Breiman L. Random forests. Machine Learning 2001; 45(1): 5-32, https://doi.org/10.1023/A:1010933404324.
• 5. Chen B, Yin P, Gao Y, Peng F. Use of the correlated EEMD and time-spectral kurtosis for bearing defect detection under large speed variation. Mechanism and Machine Theory 2018; 129: 162-174, https://doi.org/10.1016/j.mechmachtheory.2018.07.017.
• 6. Du W, Li A, Ye P, Liu C. Fault diagnosis of plunger pump in truck crane based on relevance vector machine with particle swarm optimization algorithm. Shock and Vibration 2013; 20(4): 781-792, https://doi.org/10.1155/2013/610235.
• 7. Elforjani M, Bechhoefer E. Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator.Renewable Energy 2018; 127: 258-268.
• 8. Głowacz A, Głowacz W. Vibration-Based Fault Diagnosis of Commutator Motor. Shock and Vibration 2018; art. id 7460419, https://doi.org/10.1155/2018/7460419.
• 9. Głowacz A, Głowacz Z. Diagnosis of the three-phase induction motor using thermal imaging. Infrared Physics & Technology 2017; 81: 7-16, https://doi.org/10.1016/j.infrared.2016.12.003.
• 10. Henao H, Capolino G, Manes F. Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE industrial electronics magazine 2014; 8(2): 31-42, https://doi.org/10.1109/MIE.2013.2287651.
• 11. ISA95 – Enterpise-Control System Integration Standard (https://www.isa.org/isa95/)
• 12. Jingwei G, Niaoqin H, Lehua J, Jianyi F. A New Condition Monitoring and Fault Diagnosis Method of Engine Based on Spectrometric Oil Analysis. Advances in Intelligent and Soft Computing 2011, 110:117-124, https://doi.org/10.1007/978-3-642-25185-6_16.
• 13. Korbicz J, Kościelny M (eds.).Modeling, Diagnostics and Process Control. Implementation in the DiaSter System. Springer-Verlag Berlin, Heidelberg 2011, https://doi.org/10.1007/978-3-642-16653-2.
• 14. Korbicz J, Kościelny M, Kowalczuk Z, Cholewa W (eds.). Fault Diagnosis. Models, Artificial Intelligence, Appications. Springer-Verlag Berlin Heidelberg 2004, https://doi.org/10.1007/978-3-642-18615-8.
• 15. Li Y, Liang X, Xu M, Huang W. Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform. Mechanical Systems and Signal Processing 2017, 86(Part A): 204-223.
• 16. Macián V, Tormos B, Olmeda P, Montoro L. Analytical approach to wear rate determination for internal combustion engine condition monitoring based on oil analysis. Tribology International 2003; 36: 771–776, https://doi.org/10.1016/S0301-679X(03)00060-4.
• 17. Mazurkiewicz, D. Computer-aided maintenance and reliability management systems for conveyor belts. Eksploatacja i Niezawodność-Maintenance and Reliability 2014; 16(3):377-382.
• 18. Mobley R. An Introduction to Predictive Maintenance. Second Edition. Butterworth-Heinemann 2013.
• 19. Peng Z, Chu F. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 2004; 18(2): 199-221, https://doi.org/10.1016/S0888-3270(03)00075-X.
• 20. Przystałka P, Moczulski W. Methodology of neural modelling in fault detection with the use of chaos engineering. Engineering Applications of Artificial Intelligence 2015; 41: 25-40, https://doi.org/10.1016/j.engappai.2015.01.016.
• 21. R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2014, http://www.R-project.org
• 22. Samuel P, Pines D. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration 2005;282(1-2): 475-508, https://doi.org/10.1016/j.jsv.2004.02.058.
• 23. Silva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D. High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric Power Systems Research 2018, 154: 474-483, https://doi.org/10.1016/j.epsr.2017.08.039.
• 24. Therneau T, Atkinson B. Package: rpart (http://cran.r-project.org/web/packages/rpart/rpart.pdf)
• 25. Wachla D,Moczulski W. Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases. Engineering Applications of Artificial Intelligence 2007; 20(5): 699-707, https://doi.org/10.1016/j.engappai.2006.11.002.
• 26. Wu S, Zuo M. Linear and Nonlinear Preventive Maintenance Models. IEEE Transactions on Reliability 2010; 59(1): 242-249, https://doi.org/10.1109/TR.2010.2041972.
• 27. Yan R, Gaob R, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 2014; 96(A): 1-15.
• 28. Ye Z, Wu B, Zargari N.: Online mechanical fault diagnostics of induction motor by wavelet artificial neural network using stator current. IECON Proceedings 2000; 2: 1183–1188.
• 29. Zio E. Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability 2016; 65(4): 1769-1782, https://doi.org/10.1109/TR.2016.2591504.