Abstract
Aiming at the fault diagnosis of rolling element bearings, propose a method for fine diagnosis of bearings based on wavelet transform and one-dimensional convolutional neural network. First use wavelet transform to decompose the experimental data; Use the resulting low-frequency signal as a one-dimensional convolutional neural network input, bearing fault identification. The experiment uses the deep groove ball bearing of Case Western Reserve University as the research object, Use this method to identify the normal and outer ring faults of the bearing. the result shows: This method can be effectively applied to the precise identification of bearings.
Highlights
Bearings are one of the important components in rotating machinery systems, it is widely used in major fields
It can be clearly seen that the normal signal is relatively stable, the amplitude fluctuation is small, the amplitude of the fault signal increases, and periodic glitches appear on the time domain diagram
This paper proposes a method based on the combination of wavelet transform and onedimensional convolutional neural network
Summary
Bearings are one of the important components in rotating machinery systems, it is widely used in major fields. P.D. McFadden and M.M. Toozhy combined the time-domain synchronous averaging technique with the high-frequency resonance demodulation technique to analyze the vibration signal of rolling bearings [1]. S. Prabhakarl et al Used Discrete Wavelet Transform (DWT) to analyze the outer ring and inner ring faults of rolling bearings [3]. R. Rubini et al Used continuous wavelets to extract the excellent characteristics of pulses, and used several frequency cross-section average amplitude spectra to monitor the development trend of rolling bearing faults [4]. The db wavelet is used to perform triple wavelet decomposition on the signal, and the resulting low-frequency
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