In the past few decades, wavelets have found an important place in many fields due to their nature and advantages compared to other algorithms. When the focus is on signal processing, wavelets can be used as an algorithm for de-noising, frequency analysis, feature analysis, etc. The spectrogram is one of the most common visual representations of the signal in the frequency domain when the Fourier transformation is used. Wavelets have their algorithm for signal representation in the frequency domain, and it’s called scalogram due to the nature of the scaling properties of the wavelet function.
This paper explores the application of the Continuous Wavelet Transform (CWT) for generating and analyzing scalograms. Audio signals from different direct current (DC) motor sounds are used as test data to demonstrate the effectiveness of this approach. All implementations and analyses are carried out using MATLAB software. The results highlight the advantages of wavelet-based analysis in capturing time-frequency characteristics that may not be easily observed with traditional Fourier-based methods.
References
Bialasiewicz, J. T. (2015). Application of wavelet scalogram and coscalogram for analysis of biomedical signals. Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science, 333.
Bolós, V. J., & Benítez, R. (2014). The wavelet scalogram in the study of time series. In Advances in Differential Equations and Applications (pp. 147-154). Cham: Springer International Publishing.
Damnjanović, Đ., Ćirić, D., Miletić, M., & Vujičić, D. (2020). Usage of different wavelet families in dc motor sounds feature analysis. Proceedings of the 7th International Conference on Electrical, Electronic and Computing Engineering (Ic) ETRAN, 1–1.
Damnjanović, Đ., Ćirić, D., & Perić, Z. (2019). Analysis of DC Motor Sounds Using Wavelet-Based Features. Proceedings of the 6th International Conference on Electrical, Electronic and Computing Engineering"(Ic) ETRAN, 3–6.
Damnjanović, Đ., Ćirić, D., & Perić, Z. (2021). Wavelet-Based Audio Features of DC Motor Sound. Facta Universitatis, Series: Electronics and Energetics, 34(1), 071–088.
Damnjanović, Ð. M., Ćirić, D. G., & Predić, B. B. (2018). De-noising of a room impulse response by applying wavelets. Acta Acustica United with Acustica, 104(3), 452–463.
Graps, A. (1995). An introduction to wavelets. IEEE Computational Science and Engineering, 2(2), 50–61.
Guido, R. C., Pedroso, F., Furlan, A., Contreras, R. C., Caobianco, L. G., & Neto, J. S. (2020). CWT× DWT× DTWT× SDTWT: Clarifying terminologies and roles of different types of wavelet transforms. International Journal of Wavelets, Multiresolution and Information Processing, 18(06), 2030001.
Kingsbury, N. (1999). Image processing with complex wavelets. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760), 2543–2560.
Kumar, T. K., Ogare, M. K., & Koolagudi, S. G. (2024). ECG Signal Classification using Continuous Wavelet Transform Scalogram and Convolutional Neural Network. 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT, 1–8.
Li, H., Zhao, W., Cao, H., Yao, F., & Shao, L. (2006). Measures of scale based on the wavelet scalogram with applications to seismic attenuation. Geophysics, 71(5), 111–118.
Peng, Z., Chu, F., & He, Y. (2002). Vibration signal analysis and feature extraction based on reassigned wavelet scalogram. Journal of Sound and Vibration, 253(5), 1087–1100.
Rioul, O., & Vetterli, M. (1991). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14–38.
Salles, R. S., & Ribeiro, P. F. (2023). The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification. Electric Power Systems Research, 214, 108834.
Sharan, R. V. (2020). Spoken digit recognition using wavelet scalogram and convolutional neural networks. In 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 101-105). IEEE.
Sifuzzaman, M., Islam, M. R., & Ali, M. Z. (2009). Application of wavelet transform and its advantages compared to Fourier transform. Journal of Physical Sciences, 13, 121–134.
Silik, A., Noori, M., Altabey, W. A., Ghiasi, R., & Wu, Z. (2021). Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring. Structural Durability & Health Monitoring, 15(1), 1–22.
