Abstract
Feature extraction has more importance in fault diagnosis and also to identify the important changes of rotary machines. Rolling elements are an important part of a rotary machine. The working condition of the rotary machine is based on the performance of rolling elements. Rolling element produces the fault vibration signals which are non-stationary so time-frequency distribution (TFD) is used. And time-frequency distribution is depending on Short Time Fourier Transform (STFT). This paper combines the concept of TFD and STFT. This paper also presents the different approaches of the Short-Time Fourier Transform. Another thing discussed in this paper is the real-time spectrum analysis of discrete short-time Fourier Transform. This paper is a simple analysis of the rolling element fault diagnosis problem of a rolling element with the use of TFD and STFT.
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Santhoshi, M.S., Sharath Babu, K., Kumar, S., Nandan, D. (2021). An Investigation on Rolling Element Bearing Fault and Real-Time Spectrum Analysis by Using Short-Time Fourier Transform. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_52
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DOI: https://doi.org/10.1007/978-981-15-7234-0_52
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