DEVELOPMENT AND THEORETICAL FOUNDATION OF A NOVEL HIGH-PERFORMANCE SIGNAL DIAGNOSTICS METHOD VIA WAVEFORM ANALYSIS
- Authors
-
-
Author
-
Author
-
Author
-
Sharibayev Nosir Yusupjanovich
Namangan State Technical University
Author
-
Author
-
- Keywords:
- Wavelet transform, signal diagnostics, multiscale analysis, mathematical modeling, energy spectrum, non-stationary signal, adaptive filtering.
- Abstract
-
In contemporary industrial setups, signal diagnostics forms the core of equipment condition monitoring. While traditional spectral techniques like the Fourier transform excel at stationary signal analysis, real-world production signals are typically time-variant, impulsive, and nonlinear. This research introduces a multiscale energy-based diagnostic framework leveraging wavelet transformation, with its mathematical robustness, detection sensitivity, and precise localization rigorously established through functional analysis tools.
The method outperforms conventional techniques in pinpointing local signal singularities. Simulation outcomes confirm the diagnostic metric's invariance to noise variance alongside its quadratic responsiveness to impulse perturbations. It offers a solid theoretical groundwork for predictive diagnostics in robotic production lines, pneumatic conveyors, and high-speed rotors. - References
-
[1] Oppenheim, A.V., Schafer, R.W.: Discrete-Time Signal Processing, Prentice Hall, 2010.
[2] Proakis, J.G., Manolakis, D.G.: Digital Signal Processing: Principles, Algorithms and Applications, Pearson, 2007.
[3] Cohen, L.: Time-Frequency Analysis, Prentice Hall, 1995.
[4] Mallat, S.: A Wavelet Tour of Signal Processing, Academic Press, 2009.
[5] Daubechies, I.: Ten Lectures on Wavelets, SIAM, 1992.
[6] Huang, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A, Vol. 454, pp. 903–995, 1998.
[7] Boashash, B.: Time-frequency signal analysis and processing: A comprehensive reference, Elsevier, 2016.
- Downloads
- Published
- 2026-02-26
- Issue
- Vol. 2 No. 2 (2026)
- Section
- Articles
- License
-
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Most read articles by the same author(s)
- Davlatov Abror Borijon oglu, Abdullayev Lazizbek Abdulazizovich, Shahobiddinov Bahodir Bakhtiyor oglu, Djurayev Sherzod Sobirjonovich, Sharibayev Nosir Yusupjanovich, Zharin Anatoly Lavrentievich, STOCHASTIC MODELING AND DIAGNOSTIC CHARACTERISTICS OF TENSION AND VIBROMETRIC SIGNALS FROM TECHNICAL SOURCES VIA DISCRETE WAVELET TRANSFORM , Eureka Journal of Education & Learning Technologies: Vol. 2 No. 2 (2026)