Abstract
Wavelet transform is an adaptive signal decomposition method. It inherits the respective advantages of wavelet analysis. It separates the different modalities by extracting the maximum points in the frequency domain and then adaptively divides the Fourier spectrum in the frequency domain. The bandpass filter bank is adaptively constructed to construct an orthogonal wavelet function. In this paper, the method is applied to mechanical fault diagnosis, and a mechanical fault diagnosis method based on wavelet transform is proposed. This method overcomes the shortcomings of non-stationary and nonlinear signals by using meaningless harmonic components in conventional methods. It has good time–frequency focusing characteristics and is especially suitable for analyzing nonlinear and non-stationary signals. The simulation results show that the empirical wavelet transform method is obviously superior to the BP method and can effectively decompose the natural mode of the signal. This method is less decomposed than BP and is theoretically easy to understand. Finally, the method is applied to the fault diagnosis of rotor rubbing. The experimental results further verify the effectiveness of the proposed method, which can effectively reveal the frequency structure of the friction fault data and distinguish the severity of the friction fault.
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Wang, D. (2021). Intelligent Fault Diagnosis Method of Mechanical Fault Based on Wavelet Transform. In: Chang, JW., Yen, N., Hung, J.C. (eds) Frontier Computing. FC 2020. Lecture Notes in Electrical Engineering, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-16-0115-6_229
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DOI: https://doi.org/10.1007/978-981-16-0115-6_229
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