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Języki publikacji
Abstrakty
Over the past ten years, the number of vehicles on the roads has almost doubled. The consequences of this state are negative effects, which include an increased number of road accidents and an increased level of harmful emissions into the atmosphere, along with other forms of environmental pollution. Road safety level is the sum of the safety levels of each element of the human-vehicle-environment system. In order to maintain the appropriate level of road safety, vehicles must be maintained in proper technical condition, and for this purpose, it is necessary to detect all types of damage at an early stage. All types of methods that allow non-invasive diagnosis of the technical condition of vehicles and their individual components seem to be extremely useful. It is important that they allow for detecting emerging faults in their early stages of development. Such tools undoubtedly include methods using vibration and acoustic signals as carriers of information about technical condition. Their appropriate processing and use in diagnostic systems that additionally use artificial intelligence makes it possible to meet the requirements for diagnostic systems. From a global perspective, this additionally enables the impact on reducing the costs of civilization development, including social costs. The article presents the use of various methods of processing vibroacoustic signals and artificial intelligence tools used to diagnose damage to combustion engines in cars.
Rocznik
Tom
Strony
69--87
Opis fizyczny
Bibliogr. 17 poz.
Twórcy
autor
- Faculty of Mechanical Engineering, Technical University of Košice, Letná 1/9, 042 00 Košice, Slovakia
autor
- Faculty of Transport and Aviation Engineering, The Silesian University of Technology, Krasinskiego 8 Street, 40-019 Katowice, Poland
Bibliografia
- 1. Ziakopoulos A., G. Yannis. 2020. „A review of spatial approaches in road safety”. Accident Analysis & Prevention 135: 105323.
- 2. Faus M., F. Alonso, E. Egido, M. Rezapour. 2023. „Editorial: Human factors in transport and road safety”. Frontiers in Psychology 14: 1175488.
- 3. Cociu S., Deleu R., Rimis C., Cebanu S., Cherecheș R.M. 2022. „Ethical Aspects in Road Traffic Safety and Driving Behavior Change”. Journal of Intercultural Management and Ethics 5(1): 57-69.
- 4. Cempel C.: Vibroacoustic diagnosis of machines. Warsaw: PWN. 1989.
- 5. Cempel C. 1998. „Vibroacoustical diagnostics of machinery: An outline”. Mechanical Systems and Signal Processing 2(2): 135-151.
- 6. Randall R.B., J. Antoni. 2011. „Rolling element bearing diagnostics - A tutorial”. Mechanical Systems and Signal Processing 25: 485-520.
- 7. Nawrocki W., R. Stryjski, M. Kostrzewski, W. Woźniak, T. Jachowicz. 2023. „Application of the vibro-acoustic signal to evaluate wear in the spindle bearings of machining centres. In-service diagnostics in the automotive industry”. Journal of Manufacturing Processes 92: 165-178.
- 8. Shabbir N., L. Kütt, B. Asad, M. Jawad, M.N. Iqbal, K. Daniel. 2021. „Spectrum analysis for condition monitoring and fault diagnosis of ventilation motor: a case study”. Energies 14(7): 2001.
- 9. Yang Y., Z. Peng, W. Zhang, G. Meng. 2019. „Parameterised time-frequency analysis methods and their engineering applications: A review of recent advances“. Mechanical Systems and Signal Processing 119: 182-221.
- 10. Li X., X. Liu, C. Yue, S.Y. Liang, L. Wang. 2022. „Systematic review on tool breakage monitoring techniques in machining operations”. International Journal of Machine Tools and Manufacture 176: 103882.
- 11. Gade S., K. Gram-Hansen. 1996. „Non-stationary signal analysis using wavelet transform, short-time Fourier transform and Wigner-Ville distribution”. Rewiev 2. Bruel & Kjær.
- 12. Nikias L.C., M.J. Mendel. 1993. „Signal processing with high-order spectra”. IEEE Signal Processing Magazine July 1993: 10-37.
- 13. Jasinski M., S. Radkowski. 2011. „Use of the higher spectra in the low-amplitude fatigue testing”. Mechanical Systems and Signal Processing 25(2): 704-716.
- 14. Korbicz J., Z. Kowalczuk, J.M. Kościelny, W. Cholewa. 2012. Fault Diagnosis: Models, Artificial Intelligence, Applications. Berlin, Heidelberg: Springer.
- 15. Batko W., M. Ziółko. 2002. Application of wavelet theory in technical diagnostics. Cracow: AGH Publishing House.
- 16. Manarikkal I., F. Elasha, D. Mba. 2021. „Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm”. Applied Acoustics 184: 108314.
- 17. Loutridis S., E. Douka, A. Trochidis. 2004. „Crack identification in double-cracked beams using wavelet analysis”. Journal of Sound and Vibration 277: 1025-1039.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a3548355-e8a8-4f83-b3af-860c4704dfb0
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