Labview Advanced Signal: Processing Toolkit |top|

| Method | Resolution | Computational Cost | LabVIEW VI Name | |--------|------------|--------------------|------------------| | | High | O(p³) | AR Spectrum.vi | | AR (Burg) | Very high, stable | O(Np) | Burg AR Spectrum.vi | | Maximum Entropy (MEM) | Highest | High | MEM Spectrum.vi | | MUSIC | Super-resolution for sinusoids in noise | Very high (eigen-decomposition) | MUSIC Spectrum.vi | | ESPRIT | Direct frequency estimation (no spectrum) | High | ESPRIT Frequencies.vi |

| Algorithm | Best For | LabVIEW Implementation Detail | |-----------|----------|--------------------------------| | | Stationary segments within non-stationary signals | Hanning/Hamming/Gaussian window, adjustable overlap (0–99%), output as 2D spectrogram array | | Gabor Transform | Optimal time-frequency localization | Uses Gaussian window; computes expansion coefficients via Zak transform | | Wigner-Ville Distribution (WVD) | High resolution for mono-component signals | Includes cross-term reduction (smoothed pseudo WVD) | | Choi-Williams Distribution | Reducing cross-terms while preserving resolution | Exponential kernel; adjustable kernel width parameter | | Scalogram | Wavelet-based time-frequency view | Output from Continuous Wavelet Transform (CWT) | labview advanced signal processing toolkit

A bearing fault produces a non-stationary impulse train. The STFT (spectrogram) VIs in the toolkit can reveal sidebands around the ball pass frequency that are invisible in a standard power spectrum. 3.2 Wavelet Analysis The toolkit implements both Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT) with extensive filter bank support. | Method | Resolution | Computational Cost |

= model order, N = signal length.

Measured on Intel i7-11850H, 32 GB RAM, LabVIEW 2024 64-bit. = model order, N = signal length