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Acoustic camera as a tool for identifying machinery and equipment failures

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Sound and noise are as old as humanity itself. They have accompanied civilization, evolution, and development for centuries. Music and speech represent not only the key elements of human life but also unpleasant feelings of noise that have always been an integral part of human existence. As industrial development has required more energy, powerful machinery, and equipment, there have been still noisier machines. Traffic has grown quickly due to the number and speed of vehicles. For that reason, an acoustic camera is used for the dynamic visualization of machinery and equipment noise as it analyses the sources of noise in details. Subsequently, qualified measures are introduced based on the results of the analysis. The paper considers launching another application. According to the proposed methodology, its use in identifying machinery and equipment failures and their maintenance is proved. The experiment was performed on a four-wheel lawn mower. The primary focus was on the identification of failures using an acoustic camera. The previous method allowed to quickly, precisely and efficiently identifying the failures in two out of five tested machines.
Twórcy
  • Technical University of Kosice, Letna 9, 04200 Kosice, Slovak Republic
Bibliografia
  • 1. J. Chen and R. Bond Randall, “Improved automated diagnosis of misfire in internal combustion engines based on simulation models,” Mech. Syst. Signal Process., vol. 64–65, pp. 58–83, 2015.
  • 2. M. Yuwono, Y. Qin, J. Zhou, Y. Guo, B. G. Celler, and S. W. Su, “Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model,” Eng. Appl. Artif. Intell., vol. 47, no. 2016, pp. 1–13, 2015.
  • 3. X. Jin, M. Zhao, T. W. S. Chow, and M. Pecht, “Motor bearing fault diagnosis using trace ratio linear discriminant analysis,” IEEE Trans. Ind. Electron., vol. 61, no. 5, pp. 2441–2451, 2014.
  • 4. M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3398–3407, 2013.
  • 5. Q. He, J. Wang, Y. Liu, D. Dai, and F. Kong, “Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines,” Mech. Syst. Signal Process., vol. 28, pp. 443–457, 2012.
  • 6. L. K. J. Vandamme, “Noise as a diagnostic tool for quality and reliability of electronic devices,” IEEE Trans. Electron Devices, vol. 41, no. 11, pp. 2176–2187, 1994.
  • 7. T. Yoshioka and S. Shimizu, “Monitoring of Ball Bearing Operation under Grease Lubrication Using a New Compound Diagnostic System Detecting Vibration and Acoustic Emission,” Tribol. Trans., vol. 52, no. 6, pp. 725–730, 2009.
  • 8. F. Cong, J. Chen, G. Dong, and M. Pecht, “Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis,” J. Sound Vib., vol. 332, no. 8, pp. 2081–2097, 2013.
  • 9. G. Dong and J. Chen, “Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings,” Mech. Syst. Signal Process., vol. 33, pp. 212–236, 2012.
  • 10. A. Glowacz, “Diagnostics of direct current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words,” Eksploat. i Niezawodn. – Maint. Reliab., vol. 16, no. 4, pp. 554–558, 2014.
  • 11. A. Bratek, “Possible leakage detection level in transmission pipelines using improved simplified methods,” Eksploat. i Niezawodn. – Maint. Reliab., vol. 18, no. 3, pp. 469–480, 2016.
  • 12. S. Radkowski And K. Szczurowski, “Use of vibroacoustic signals for diagnosis of pre- stressed structures,” Eksploat. i Niezawodn. – Maint. Reliab., vol. 14, no. 1, pp. 84–91, 2012.
  • 13. Grega, Robert; Krajnak, Jozef; Zul‘ova, Lucia; et al. Failure analysis of driveshaft of truck body caused by vibrations. Engineering Failure Analysis 2017, 79, 208–215.
  • 14. Gabriel Fedorko, Pavol Liptai, Vieroslav Molnár: Proposal of the methodology for noise sources identification and analysis of continuous transport systems using an acoustic camera. Engineering Failure Analysis 2018, 83, 30–46, https://doi. org/10.1016/j.engfailanal.2017.09.011.
  • 15. Rudawska, Anna; Debski, Hubert: Experimental And Numerical Analysis Of Adhesively Bonded Aluminium Alloy Sheets Joints. Eksploatacja I Niezawodnosc-Maintenance And Reliability 2011, 1, 4–10.
  • 16. Falkowicz, Katarzyna; Ferdynus, Miroslaw; Debski, Hubert: Numerical Analysis Of Compressed Plates With A Cut-Out Operating In The Geometrically Nonlinear Range. Eksploatacja I Niezawodnosc- Maintenance And Reliability 2015, 17, 2, 222–227.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a4e3ba6-285c-4d88-a140-a309992fb191
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