pyRaTS 0.21

processing of (RAndom) TimeSeries for vibration fatigue

pyRaTS - processing of (RAndom) TimeSeries for vibration fatigue

Providing an object-oriented framework to analyze and process time series with the focus on random vibration fatigue. PyRaTS is capable of handling single- and multi-channel, as well as single- and multi-process time series configurations. Implementation of the non-stationarity matrix, the Fatigue Damage Spectrum and quasi-stationary signal definitions to deal with the challenges of non-stationary loading.

Installing this package

Use pip to install it by:

.. code-block:: console

$ pip install pyRaTS

Simple example

Here is a simple example for running a basic code. Further examples can be found on: https://github.com/ArvidTrapp/pyRaTS

.. code-block:: python

import pyRaTS as ts
import numpy as np

# Defining the series by pseudo-random generator...
T  = 10
fs = 1024
N  = T*fs
x  = np.random.randn(N)

# Initialize series and some basic plots...
sig = ts.timeseries(x,name = 'sample timeseries', fs = fs)
sig.plot()
sig.plot_prob()
sig.plot_psd()

# derive response series and some further basic plots...
respsig = sig.der_sdofResponse(fD = 50)
respsig.plot_psd()
respsig.plot_ls()

Some methods for a statistical analysis of random time series / estimation of statistical descriptors

* spectral moments (est_specMoms)
* Dirlik estimator (est_dirlik/est_dirlikD)
* PSD (est_psd)
* load spectra (est_ls)
* Fatigue Damage Spectrum (est_fds) ...accepts list of FLife methods for damage estimation
* non-stationarity matrix (est_nonstat)

Some methods for plotting

* time series (plot)
* PSD (plot_psd)
* absolute of Fourier transform (plot_X)
* load spectra (plot_ls) ...accepts list of FLife methods with PDF definition
* transfer function (plot_tf)
* Fatigue Damage Spectrum (plot_fds) 
* non-stationarity matrix (plot_nonstat)

Some methods for processing time series

* statistical response...PSD & Non-stat.-Matrix (der_statResponse(f,H)) 
* response timeseries of single-degree-of-freedom system (der_sdofResponse(fD, D, func))
* response timeseries for linear transfer function (der_response(f,H))
* quasi-stationary load definition on the basis of the load spectra of the Fatigue Damage Spectrum (der_lsEquivalent())
* load definition on the basis of the inverse Fatigue Damage Spectrum (der_iFDS())
* ideal high pass filtered signal (der_highpass(f))
* ideal low pass filtered signal  (der_lowpass(f))
* ideal band pass filtered signal (der_bandpass(f))