Bearing Signal Background Noise Denoising Using Wavelet Transform, EMD, and Their Hybrid Approach
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To perform background noise denoising analysis on bearing signals, we can utilize wavelet transform, empirical mode decomposition (EMD), and their hybrid approach. Wavelet transform enables extraction of different frequency components from signals through multi-resolution analysis, typically implemented using functions like wavedec() for decomposition and waverec() for reconstruction in signal processing libraries. EMD adaptively decomposes signals into multiple intrinsic mode functions (IMFs) using sifting algorithms that identify local extrema and envelopes. The hybrid approach combines wavelet's frequency localization with EMD's adaptive decomposition, often involving wavelet preprocessing followed by EMD denoising of specific coefficients or parallel processing with decision fusion. This comprehensive methodology provides more accurate background noise analysis and removal while preserving critical bearing fault characteristics.
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