FatigueDS
-----------------------
Calculating Extreme Response Spectrum (ERS) and Fatigue Damage Spectrum (FDS) of signals.
Calculations are supported for sine, sine-sweep, and random signals (defined using PSD or time history).
The underlying theory is based on [1].
See the `documentation <https://fatigueds.readthedocs.io/en/latest/index.html>`_ for more information.
Installation
------------------
Use `pip` to install it by:
.. code-block:: console
$ pip install FatigueDS
Usage
------------------
Some short examples of how to use the package are given below for different types of signals.
Random signals (PSD)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Here is an example of determining the ERS and FDS of a random signal, defined in the frequency domain (PSD):
Generate sample signal PSD:
.. code-block:: python
import numpy as np
import pyExSi as es
# generate random signal
fs = 5000 # sampling frequency [Hz]
time = 1 # time duration [s]
freq_flat = np.arange(0, fs / 2, 1 / time) # frequency vector
freq_lower = 200 # PSD lower frequency limit [Hz]
freq_upper = 1000 # PSD upper frequency limit [Hz]
PSD_flat = es.get_psd(freq_flat, freq_lower, freq_upper, variance=800) # one-sided flat-shaped PSD
Use the package:
.. code-block:: python
import FatigueDS
# instantiate the SpecificationDevelopment class
# set the frequency range (start, stop, step) and damping ratio
sd = FatigueDS.SpecificationDevelopment(freq_data=(100, 1100, 20), damp=0.05)
# set the random load
sd.set_random_load((PSD_flat, freq_flat), unit='ms2', T=3600) # input is PSD array and frequency array
# calculate the ERS and FDS
sd.get_ers()
sd.get_fds(b=10, C=1e80, K=6.3 * 1e10)
# plot the results
sd.plot_ers()
sd.plot_fds()
# or access the results directly
ers = sd.ers
fds = sd.fds
f = sd.f0_range # frequency vector
Random signals (time history)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Here is an example of determining the ERS and FDS of a random signal, defined in the time domain. For time domain, two methods are available:
- Convolution (directly from time history, using rainflow counting)
- PSD averaging (converting time history to PSD and then to ERS and FDS)
Import random time history data:
.. code-block:: python
import numpy as np
_time_data = np.load('test_data/test_time_history.npy', allow_pickle=True)
time_history_data = _time_data[:,1]
t = _time_data[:,0]
dt = t[2] - t[1]
Use the package:
.. code-block:: python
import FatigueDS
# instantiate the SpecificationDevelopment classes
sd_1 = FatigueDS.SpecificationDevelopment(freq_data=(20, 200, 5)) # convolution
sd_2 = FatigueDS.SpecificationDevelopment(freq_data=(20, 200, 5)) # psd averaging
# set the random loads (input is time history array and time step)
sd_1.set_random_load((time_history_data, dt), unit='g', method='convolution')
sd_2.set_random_load((time_history_data, dt), unit='g', method='psd_averaging', bins=10)
# calculate the ERS and FDS
sd_1.get_ers()
sd_1.get_fds(b=10, C=1e80, K=6.3 * 1e10)
sd_2.get_ers()
sd_2.get_fds(b=10, C=1e80, K=6.3 * 1e10)
# plot the results
sd_1.plot_ers(label='Time history (convolution)')
sd_2.plot_ers(new_figure=False, label='Time history (PSD averaging)')
sd_1.plot_fds(label='Time history (convolution)')
sd_2.plot_fds(new_figure=False, label='Time history (PSD averaging)')
# or access the results directly
ers_1 = sd_1.ers
fds_1 = sd_1.fds
f_1 = sd_1.f0_range # frequency vector
ers_2 = sd_2.ers
fds_2 = sd_2.fds
f_2 = sd_2.f0_range # frequency vector
Sine and sine-sweep signals
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Here is an example of determining the ERS and FDS of a sine and sine-sweep signal:
.. code-block:: python
import numpy as np
import FatigueDS
import matplotlib.pyplot as plt
# instantiate classes
sd_sine = FatigueDS.SpecificationDevelopment(freq_data=(0, 2000, 5), damp=0.1) # sine
sd_sine_sweep = FatigueDS.SpecificationDevelopment(freq_data=(0, 2000, 5), damp=0.1) # sine sweep
# set the sine and sine-sweep loads
sd_sine.set_sine_load(sine_freq=500, amp=10, t_total=3600) # t_total is is required only for FDS calculation.
sd_sine_sweep.set_sine_sweep_load(const_amp=[5, 10, 20], const_f_range=[20, 100, 500, 1000], exc_type='acc', sweep_type='log', sweep_rate=1)
# calculate the ERS and FDS
sd_sine.get_ers()
sd_sine_sweep.get_ers()
sd_sine.get_fds(b=10, C=1e80, K=6.3 * 1e10)
sd_sine_sweep.get_fds(b=10, C=1e80, K=6.3 * 1e10)
# plot the results
sd_sine.plot_ers(label='sine')
sd_sine.plot_fds(label='sine')
sd_sine_sweep.plot_ers(label='sine sweep')
sd_sine_sweep.plot_fds(label='sine sweep')
References:
1. C. Lalanne, Mechanical Vibration and Shock: Specification development,
London, England: ISTE Ltd and John Wiley & Sons, 2009
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"description": "FatigueDS\n-----------------------\n\nCalculating Extreme Response Spectrum (ERS) and Fatigue Damage Spectrum (FDS) of signals. \nCalculations are supported for sine, sine-sweep, and random signals (defined using PSD or time history).\nThe underlying theory is based on [1].\n\nSee the `documentation <https://fatigueds.readthedocs.io/en/latest/index.html>`_ for more information.\n\n\nInstallation\n------------------\n\nUse `pip` to install it by:\n\n.. code-block:: console\n\n $ pip install FatigueDS\n\nUsage\n------------------\nSome short examples of how to use the package are given below for different types of signals.\n\nRandom signals (PSD)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nHere is an example of determining the ERS and FDS of a random signal, defined in the frequency domain (PSD):\n\nGenerate sample signal PSD:\n\n.. code-block:: python\n\n import numpy as np\n import pyExSi as es\n\n # generate random signal\n fs = 5000 # sampling frequency [Hz]\n time = 1 # time duration [s]\n freq_flat = np.arange(0, fs / 2, 1 / time) # frequency vector\n freq_lower = 200 # PSD lower frequency limit [Hz]\n freq_upper = 1000 # PSD upper frequency limit [Hz]\n PSD_flat = es.get_psd(freq_flat, freq_lower, freq_upper, variance=800) # one-sided flat-shaped PSD\n\nUse the package:\n\n.. code-block:: python\n \n import FatigueDS\n\n # instantiate the SpecificationDevelopment class \n # set the frequency range (start, stop, step) and damping ratio\n sd = FatigueDS.SpecificationDevelopment(freq_data=(100, 1100, 20), damp=0.05)\n\n # set the random load\n sd.set_random_load((PSD_flat, freq_flat), unit='ms2', T=3600) # input is PSD array and frequency array\n\n # calculate the ERS and FDS\n sd.get_ers()\n sd.get_fds(b=10, C=1e80, K=6.3 * 1e10)\n \n # plot the results\n sd.plot_ers()\n sd.plot_fds()\n\n # or access the results directly\n ers = sd.ers\n fds = sd.fds\n f = sd.f0_range # frequency vector\n \n\nRandom signals (time history)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nHere is an example of determining the ERS and FDS of a random signal, defined in the time domain. For time domain, two methods are available:\n - Convolution (directly from time history, using rainflow counting)\n - PSD averaging (converting time history to PSD and then to ERS and FDS)\n\nImport random time history data:\n\n.. code-block:: python\n\n import numpy as np\n\n _time_data = np.load('test_data/test_time_history.npy', allow_pickle=True)\n time_history_data = _time_data[:,1]\n t = _time_data[:,0] \n dt = t[2] - t[1]\n\nUse the package:\n\n.. code-block:: python\n\n import FatigueDS\n \n # instantiate the SpecificationDevelopment classes\n sd_1 = FatigueDS.SpecificationDevelopment(freq_data=(20, 200, 5)) # convolution\n sd_2 = FatigueDS.SpecificationDevelopment(freq_data=(20, 200, 5)) # psd averaging\n\n # set the random loads (input is time history array and time step)\n sd_1.set_random_load((time_history_data, dt), unit='g', method='convolution')\n sd_2.set_random_load((time_history_data, dt), unit='g', method='psd_averaging', bins=10)\n\n # calculate the ERS and FDS\n sd_1.get_ers()\n sd_1.get_fds(b=10, C=1e80, K=6.3 * 1e10)\n\n sd_2.get_ers()\n sd_2.get_fds(b=10, C=1e80, K=6.3 * 1e10)\n\n # plot the results\n\n sd_1.plot_ers(label='Time history (convolution)')\n sd_2.plot_ers(new_figure=False, label='Time history (PSD averaging)')\n \n sd_1.plot_fds(label='Time history (convolution)')\n sd_2.plot_fds(new_figure=False, label='Time history (PSD averaging)')\n\n # or access the results directly\n\n ers_1 = sd_1.ers\n fds_1 = sd_1.fds\n f_1 = sd_1.f0_range # frequency vector\n\n ers_2 = sd_2.ers\n fds_2 = sd_2.fds\n f_2 = sd_2.f0_range # frequency vector\n\nSine and sine-sweep signals\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nHere is an example of determining the ERS and FDS of a sine and sine-sweep signal:\n\n.. code-block:: python\n\n import numpy as np\n import FatigueDS\n import matplotlib.pyplot as plt\n\n # instantiate classes\n sd_sine = FatigueDS.SpecificationDevelopment(freq_data=(0, 2000, 5), damp=0.1) # sine\n sd_sine_sweep = FatigueDS.SpecificationDevelopment(freq_data=(0, 2000, 5), damp=0.1) # sine sweep\n\n # set the sine and sine-sweep loads\n sd_sine.set_sine_load(sine_freq=500, amp=10, t_total=3600) # t_total is is required only for FDS calculation.\n sd_sine_sweep.set_sine_sweep_load(const_amp=[5, 10, 20], const_f_range=[20, 100, 500, 1000], exc_type='acc', sweep_type='log', sweep_rate=1)\n\n # calculate the ERS and FDS\n sd_sine.get_ers()\n sd_sine_sweep.get_ers()\n\n sd_sine.get_fds(b=10, C=1e80, K=6.3 * 1e10)\n sd_sine_sweep.get_fds(b=10, C=1e80, K=6.3 * 1e10)\n\n # plot the results\n sd_sine.plot_ers(label='sine')\n sd_sine.plot_fds(label='sine')\n \n sd_sine_sweep.plot_ers(label='sine sweep')\n sd_sine_sweep.plot_fds(label='sine sweep')\n\n\nReferences:\n 1. C. Lalanne, Mechanical Vibration and Shock: Specification development,\n London, England: ISTE Ltd and John Wiley & Sons, 2009\n",
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