Abstract
The predictive maintenance of rotating machines helps to prevent economic loss and personal damage. Vibrational data of bearing is used to measure the early-stage faults but sometimes this vibrational data consists of many unwanted signals that need to be removed before applying the signal processing techniques. This paper proposes an algorithm to design a dynamic filter that can adjust its bandwidth and central frequency according to different faults that occurs in the rotating bearing. The harmony search algorithm is used to optimize the bandpass filter parameters according to the faults and the short-time Fourier trans-form-based Spectral Kurtosis helps measure the strength of fault frequencies. The filter parameters are selected that correspond to the maximum value of the short-time Fourier transform-based Spectral Kurtosis. The simulation results verify the performance of the proposed algorithm.
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Ahsan, M., Bismor, D. (2022). Early-Stage Faults Detection Using Harmony Search Algorithm and STFT-Based Spectral Kurtosis. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2022: New Solutions and Technologies for Automation, Robotics and Measurement Techniques. AUTOMATION 2022. Advances in Intelligent Systems and Computing, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-031-03502-9_8
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DOI: https://doi.org/10.1007/978-3-031-03502-9_8
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