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With FlexPro's Spectral Analysis Option, you enter a new world of software engineering. You'll save precious time by eliminating the programming or multi-step UI procedures that are normally required for performing sophisticated spectral analysis. By using real-time 2D and 3D spectral graphs, the new FlexPro's Analysis Wizard offers immediate visual feedback when changing algorithms, algorithm parameters, and spectral formats.
Quickly locate your signal components
FlexPro's Spectral Analysis Option gives engineers and researchers the power to rapidly find the components of complex signals. A rich set of spectral analysis procedures helps you make intelligent signal content conclusions for any application. The built-in spectral analysis procedures include: FFT, AR, ARMA, Eigenanalysis, Continuous Wavelets, Cross-Spectra, Coherence, and Transfer Function Estimation.
Identify frequency and power with Fourier analysis
Get a complete picture of the frequency signature of a signal using up to five different Fourier spectrum methods. Solve the leakage problem found with a standard FFT by using one of the thirty built-in data-tapering windows. The latest innovations in algorithms, adaptive spectra, and peak determination help you to better characterize the frequency and power of each signal component. You can even manage unevenly spaced data with Fourier techniques originally developed by astrophysicists.
Effortlessly analyze non-stationary data
Simultaneously find the time and frequency localization components of a non-stationary periodic signal with Short-Time Fourier Transform or Continuous Wavelet Transform methods. For the CWT, the Spectral Analysis Option gives you a choice of three adjustable wavelets in order to find the optimum time-frequency resolution tradeoff.
Principal component modeling
The Spectral Analysis Option offers state of the art methods for isolating the spectra of the principal components within a signal. These methods remove of the influence of noise in the AR SVD, ARMA SVD, and Eigen-decomposition procedures, enabling you to optimize the estimation of narrowband components.
Harmonic analysis
Advanced parametric sinusoidal modeling is offered with your choice of frequency estimation methods. The number of harmonics or spectral peaks can be set directly by count or indirectly by spectral threshold.
Cepstral Analysis
The Cepstrum and its minimum-phase reconstruction can be used to de-convolve signals. Its main applications are speech analysis and echo detection.
Shock Response Spectra (SRS)
Use the Shock Response Spectrum (SRS) to estimate the damage potential of mechanical impulses or stationary random vibrations.
To calculate shock response spectra, an acceleration signal is used for primary excitation of a series of single degree of freedom (SDOF) systems with given natural frequencies. The spectra are formed by the absolute maxima, maxima or minima of these systems responses.
Features
Fourier Spectral Analysis
- Procedures: windowed Fourier spectrum, periodogram, peak-hold spectrum, Fourier multitaper, spectrum of unevenly sampled data
- Transforms: best exact n method automatically chosen from four different algorithms (radix2, prime factor, mixed radix, chirp-Z)
- Spectral formats include: amplitude, RMS amplitude, amplitude², magnitude, magnitude², phase, dB, normalized dB, PSD, TISA, MSA, SSA, variance, complex, real part and imaginary part
- Options for zero padding and to display white noise critical limits
- Data tapering windows, 21 fixed width, 9 adjustable width including Kaiser-Bessel, VanderMaas, Chebyshev, and Slepian DPSS
- Fourier peak detection by bin interpolation
AR, ARMA and Eigen Spectral Procedures
- Autoregressive (AR) spectral estimators: autocorrelation, maximum entropy (Burg), least-squares normal equations, least-squares covariance and modified covariance, SVD principal component AR
- Autoregressive-Moving-Average (ARMA) spectral estimators, including non-linear optimization and SVD principal component methods for signal-noise separation
- Eigenanalysis methods: MUSIC (Multiple Signal Classification), EV (Eigenvector)
- Select signal and noise sub-spaces for SVD or Eigen-based signal noise thresholding
- Peak detection by complex roots of AR polynomial or eigenmodes
- Adaptive spectra using Runge-Kutta algorithm to accurately map sharp spectral peaks, minimize spectrum length
Time-Frequency Spectral Analysis
- Short-Time Fourier Transform (STFT) spectrum
- Peak-Hold STFT
- Continuous Wavelet Transform (CWT) spectrum multi-resolution time-frequency techniques
- Wavelet spectra can be generated with up to 1000 linear or logarithmic frequencies, range of frequencies can be customized
- Adjustable mother wavelets: Morlet, Paul, Gaussian Derivative
- Offers capability of ultra high frequency resolution with very large signals
Harmonic Analysis
- Sinusoid or damped-sinusoid modeling using automatic, Fourier, AR, Eigen, or Prony algorithms for frequency estimation
- Harmonics table, THD, SNR, SINAD and de-noised signal
Two-Signal Spectral Analysis
- Fourier windowed cross-spectra and Fourier cross-periodogram
- Coherence, including SNR spectra
- Fourier domain transfer function
Non-linear methods
- Real cepstrum including "liftering" and minimum-phase reconstruction.
Shock Response Spectrum (SRS) Analysis
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Maximax, initial and residual spectrum for maxima, minima and absolute maxima
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SDOF system responses can also be calculated as an alternative to spectra
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Spectra for acceleration, velocity and displacement
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Customizable linear or logarithmic frequency division
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Damping can be specified as a damping ratio or quality factor
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Simple selection of the shock event using cursors in the Analysis Wizard
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