Comparative analysis of denoising techniques for vibration signals in rotating machinery

Vibration signals in rotating machinery are frequently contaminated by noise, which compromises data quality and leads to the loss of valuable signal information. Consequently, effective noise reduction is essential for accurate signal analysis. This study presents a comparative analysis of various noise reduction methods applied to vibration signals in rotating machinery. The methods evaluated include compressed sensing with dictionary bases and multiple reconstruction algorithms, discrete and wavelet packet denoising using different mother wavelets and thresholding techniques, filtering methods, hybrid approaches combining wavelet denoising with compressed sensing, and mode decomposition based methods including empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition (CEEMD) integrated with wavelet denoising. Detrended fluctuation analysis (DFA) and filtering techniques are also investigated. A key challenge in compressed sensing is the uncertainty associated with signal sparsity estimation, particularly in noisy or near sparse signals, which can affect reconstruction accuracy. Performance metrics including mean squared error (MSE), mean absolute error (MAE), signal to noise ratio (SNR), peak signal to noise ratio (PSNR), cross correlation, and computational speed are employed to evaluate denoising performance and optimal sparsity levels. The results indicate that compressed sensing based approaches enable effective noise reduction while significantly reducing data size, preserving signal reconstruction quality under high compression conditions. The results further demonstrate that hybrid wavelet denoising and compressed sensing techniques provide superior denoising performance compared to individual methods, and that the choice of an appropriate dictionary matrix significantly enhances both sparsification and reconstruction accuracy.