*This blog entry is a work-in-progress.*

Fatigue analysis can be performed in the time domain using rainflow cycle counting. This is an elegant, brute-force approach. One advantage of the time domain approach is that it tends to highlight the occurrences of peaks above 3-sigma for the case of random vibration.

These higher peaks can also be accounted for in frequency domain methods, but the frequency domain approach requires a more thorough consideration of the Rayleigh distribution and other statistical theory.

Furthermore, the time domain method is better able to handle nonstationary and non-Gaussian time history inputs.

Here is a paper that gives a fatigue analysis example for a single-degree-of-freedom system subjected to a base input PSD using Rainflow cycle counting in the time domain: Miners_fatigue_rainflow.pdf

The method can readily be extended to the case of a multi-degree-of-freedom or a continuous system, as will be shown in future papers posted at this blog entry.

* * *

rainflow_bins.m is a Matlab script that performs rainflow cycle counting on a time history per ASTM E 1049-85 (2005).

A Matlab GUI version is included in: Vibrationdata Matlab Signal Analysis

Package

A Matlab MEX version is given at: Matlab MEX Rainflow

* * *

See also:

Synthesize a Time History to satisfy a PSD

Rainflow Cycle Counting

Shock Response Spectrum

Python Rainflow Fatigue

Aircraft Fatigue

MMPDS-01

Fatigue Damage Spectrum

* * *

– Tom Irvine

Contact Form:

### Like this:

Like Loading...

Pingback: Rainflow Fatigue | vibrationdatapython