References
- AGLETDINOV, E. – POMPONI, E. – MERSON, D. – VINOGRADOV, A. 2016. A novel Bayesian approach to acoustic emission data analysis. In Ultrasonics, no. 72, pp. 89–94. DOI: https://doi.org/10.1016/j.ultras.2016.07.014
- ALSHORMAN, O. – ALKAHATNI, F. – MASADEH, M. – IRFAN, M. – GLOWACZ, A. – ALHOBIANI, F. – KOZIK, J. – GLOWACZ, W. 2021. Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. In Advances in Mechanical Engineering, vol. 13, no. 2. DOI: https://doi.org/10.1177/1687814021996915
- BAGHERI, A. – TARIGHI, J. – EMAMI, N. – SZYMANEK, M. 2024. Extension experts´ intentions to use precision agricultural technologies, a test with the technology acceptance model. In Acta Technologica Agriculturae, vol. 27, no. 2, pp. 84–91. DOI: https://doi.org/10.2478/ata-2024-0012
- BARAT, V. A. – CHERNOV, D. V. – ELIZAROV, S. V. 2016. Discovering data flow discords for enhancing noise immunity of acoustic-emission testing. In Russian Journal of Nondestructive Testing, vol. 52, no. 6, pp. 347–356. DOI: https://doi.org/10.1134/S1061830916060024
- BERANEK, L. – MELLOW, T. 2019. Acoustics: Sound Fields, Transducers and Vibration. UK : Elsevier, Academic Press, 900 pp. ISBN 978-0-12-815227-0. DOI: https://doi.org/10.1016/C2017-0-01630-0
- BERTAGNOLIO, F. – MADSEN, H. A. – FISCHER, A. – BAK, CH. 2017. A semi-empirical airfoil stall noise model based on surface pressure measurements. In Journal of Sound and Vibration, vol. 387, pp. 127–162. DOI: https://doi.org/10.1016/j.jsv.2016.09.033
- BUILO, S. I. 2013. Physical, mechanical and statistical aspects of acoustic emission diagnostics. In Physics and Mechanics of New Materials and Their Applications. New York : Nova Science Publishers, no. 15, pp. 171–184.
- DANYUK, A. – MERSON, D. – VINOGRADOV, A. 2014. New prospects to use acoustic emission during scratch testing for probing fundamental mechanisms of plastic deformation. In The 12th International Conference of the Slovenian Society for Non-Destructive Testing “Application of Contemporary Non-Destructive Testing in Engineering”, 4–6 September 2013, Portorož, Slovenia, pp. 567–574.
- DANYUK, A. V. – RASTEGAEV, I. A. – MERSON, D. L. – VINOGRADOV, A. 2017. Advanced-reliability acoustic-emission transducers. In Russian Journal of Nondestructive Testing, vol. 53, no. 1, pp. 32–38.
- CHAN, P. K. – STOLFO, S. J. 1998. Toward scalable learning with nonuniform class and cost distributions: A case study in credit card fraud detection. In KDD´98 Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, USA, 27–31 August 1998, pp. 164–168.
- GLOWACZ, A. 2018. Acoustic based fault diagnosis of three-phase induction motor. In Applied Acoustics, vol. 137, pp. 82–89. DOI: https://doi.org/10.1016/j.apacoust.2018.03.010
- JIANG, C. – SONG, Q. – GUO, D. – LI, H. 2014. Estimation algorithm of minimum dwell time in precision cylindrical plunge grinding using acoustic emission signal. In International Journal of Precision Engineering and Manufacturing, vol. 15, pp. 601–607. DOI: https://doi.org/10.1007/s12541-014-0377-y
- KOVACEVIC, R. – MOMBER, A. W. – MOHEN, R. S. 2002. Energy dissipation control in hydro-abrasive machining using quantitative acoustic emission. In The International Journal of Advanced Manufacturing Technology, vol. 20, no. 6, pp. 397–406. DOI: https://doi.org/10.1007/s001700200169
- LACAGNINA, G. – CHAITANYA, P. – KIM, J.-H. – BERK, T. – JOSEPH, P. – CHOI, K.-S. GANAPATHISUBRAMANI, B. – HASHEMINEJAD, S. M. – CHONG, T. P. – STALNOV, O. SHAHAB, M. F. – OMIDYEGANEH, M. – PINELLI. A. 2021. Leading edge serrations for the reduction of aerofoil self-noise at low angle of attack, pre-stall and post-stall conditions. In International Journal of Aeroacoustics, vol. 20, no. 1–2, pp. 130–156. DOI: https://doi.org/10.1177/1475472X20978379
- LIU, J. – JIANG, C. – YANG, X. – SUN, S. 2024. Review of the application of acoustic emission technology in green manufacturing. In International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 11, no. 3, pp. 995–1016. DOI: https://doi.org/10.1007/s40684-023-00557-w
- NARAYANAN, S. – SINGH, S. K. – KANT, M. – NARAYANMURTHY, A. 2024. Control of fan broadband noise through wavy leading and trailing edge serrations. In International Journal of Aeroacoustics, vol. 24, no. 1–2, pp. 90–103. DOI: https://doi.org/10.1177/1475472X241306316
- NASHED, M. S. – STEEL, J. A. – REUBEN, R. L. 2013. The use of acoustic emission for the condition assessment of gas turbines: Acoustic emission generation from normal running. In Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 228, no. 4, pp. 286–308. DOI: https://doi.org/10.1177/0954408913502167
- O´BRIEN, R. J. – FONTANA, J. M. – PONSO, N. – MOLISANI, L. 2017. A pattern recognition system based on acoustic signals for fault detection on composite materials. In European Journal of Mechanics – A/Solids, vol. 64, pp. 1–10. DOI: https://doi.org/10.1016/j.euromechsol.2017.01.007
- OHTSU, M. 2015. Acoustic Emission (AE) and Related Nondestructive Evaluation (NDE) Techniques in the Fracture Mechanics of Concrete. Fundamentals and Applications. UK : Elsevier, Woodhead Publishing, 318 рp. ISBN 978-1-78242-327-0. DOI: https://doi.org/10.1016/C2014-0-02667-6
- POMPONI, E. – VINOGRADOV, A. – DANYUK, A. 2015. Wavelet based approach to signal activity detection and phase picking: Application to acoustic emission. In Signal Processing, no. 115, pp. 110–119. DOI: https://doi.org/10.1016/j.sigpro.2015.03.016
- POMPONI, E. – VINOGRADOV, A. 2013. A real-time approach to acoustic emission clustering. In Mechanical Systems and Signal Processing, vol. 40, no. 2, pp. 791–804. DOI: https://doi.org/10.1016/j.ymssp.2013.03.017
- RANI, M. – DHOK, S. – DESHMUKH, R. 2020. A machine condition monitoring framework using compressed signal processing. In Sensors, vol. 20, no. 1, article no. 319. DOI: https://doi.org/10.3390/s20010319
- SHAO, H. – JIANG, H. – LI, X. – WU, S. 2018. Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. In Knowledge-Based Systems, vol. 140, pp. 1–14. DOI: https://doi.org/10.1016/j.knosys.2017.10.024
- ZHANG, Y. – WANG, T. – PAN, W.-P. – ROMERO, C. E. 2019a. Advances in Ultra-Low Emission Control Technologies for Coal-Fired Power Plants. UK : Elsevier, Woodhead Publishing, 273 рp. ISBN 978-0-08-102418-8. DOI: https://doi.org/10.1016/C2017-0-00932-1
- ZHANG, J. – YANG, S. – HAO, R. – GU, X. 2019b. Amplitude attenuation laws of acoustic emission waves in plate structures. In Dyna, vol. 94, no. 1, pp. 67–74. DOI: http://dx.doi.org/10.6036/8987
- ZHAO, X. – JIA, M. – LIN, M. 2020. Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. In Measurement, vol. 152, article no. 107320. DOI: https://doi.org/10.1016/j.measurement.2019.107320
- ŽITŇÁK, M. – LENDELOVÁ, J. – PIVARČIOVÁ, Z. – KORENKO, M. – KIEŁBASA, P. – DOSTÁL, P. 2023. Possibilties of noise load elemination in production. In Acta Technologica Agriculturae, vol. 26, no. 1, pp. 42–48. DOI: https://doi.org/10.2478/ata-2023-0006
- WANG, W.-J. – CUI, L.-L. – CHEN, D.-Y. 2016. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault. In Acta Mechanica Sinica, vol. 32, pp. 265–272. DOI: https://doi.org/10.1007/s10409-015-0529-z
- WU, Z. – GUO, Y. – LIN, W. – YU, S. – JI, Y. 2018. A weighted deep representation learning model for imbalanced fault diagnosis in cyber-physical systems. In Sensors, vol. 18, no. 4, article no. 1096. DOI: https://doi.org/10.3390/s18041096
- XU, B. – WU, J. – WANG, M. 2017. Study of modal acoustic emission to monitor the impact damage in a composite plate. In Journal of Vibroengineering, vol. 19, no. 5, pp. 3335–3348. DOI: https://doi.org/10.21595/jve.2017.17879