Optimal Sparse Signals from CNC Machine Vibration

This article discusses an experimental approach to obtaining sparse signals from vibration data in Computer Numerical Control (CNC) machines with X-Y-Z axes. The experiment focuses on manipulating vibration data into sparse signals using dynamic threshold and transform options, including Fast Fourier Transform (FFT), Walsh-Hadamard Transform (WHT), and Discrete Cosine Transform (DCT). Based on the experimental results, it is found that the optimal transformation for the X and Z axes is DCT. This transformation yields a PSNR of 66.89 dB with sparsity of 97.41 percent for the X axis and a PSNR of 55.84 dB with sparsity of 96.67 percent for the Z axis. For the Y-axis vibration, the optimal transformation is FFT, which results in a PSNR of 76.29 dB with sparsity of 98.15 percent. These vibration data are transformed using dynamic sparse parameters based on the maximum signal value for each axis. It is essential to determine the transformation methods and thresholds to obtain sparse signals before using them in compressive sensing applications.