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• W2079329229 abstract "Bearing Faults in rotating machinery occur as low energy impulses in their vibration signal and are lost in the noise. This signal has to be properly denoised before analyzing for effective condition monitoring. This paper proposes a novel method to denoise and analyze such a noisy signal. The Undecimated Discrete Wavelet Transform (UDWT) with Morlet wavelet based De-noising method is used to denoise the signal. Then this denoised signal is decomposed by Empirical Mode Decomposition (EMD) into a number of Intrinsic Mode Functions (IMF). The impulses in the signal, corresponding to the characteristic fault frequency, are seen clearly in the FFT of the IMFs. A Fast Fourier Transform (FFT), Wavelet Transform (WT), Empirical Mode Decomposition and Envelope Detection are also performed with the acquired signal and all the results are compared with the proposed method. These results clearly show the effectiveness of proposed method in detecting the faults." @default.
• W2079329229 created "2016-06-24" @default.
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• W2079329229 date "2013-06-15" @default.
• W2079329229 modified "2023-10-14" @default.
• W2079329229 title "Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis" @default.
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• W2079329229 doi "https://doi.org/10.7251/els1317001r" @default.
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