User: unknown  ||   Login  ||  NEWS: FABER funded!, pyLife, Lecture by P. Yadegari, WCFA2023, TFP to watch

 

Fatigue Lounge

News

Conference List

Photo Gallery

Reports

Links

Glossary

 

Title PragTic

Concept

PragTic SW

Author

FAQs

Bugs

Users

User Profile

 

Suporting Institutions

Sponsors

 

FME CTU in Prague

Introduction

The strain records found below are used for presenting the ideas highlighted within the paper linked below. It shows practical solutions how to recognize stationary blocks within a non-stationary signal, or how to estimate the confidence interval of the resulting damage, which in turn can be used to derive adequate safety factors.

The paper is published in the Open Access scheme, so you are invited to read it.

Jan Papuga (on behalf of the team)

Conditions of Use

In case you decide to use these data items for any your publication, you agree to refer to the original paper, where they are first used:

Marques, J. M. E; Benasciutti, D.; Papuga, J.; Růžička, M.: Uncertainty of estimated rainflow damage in stationary random loadings and in those stationary per partes, Applied Sciences, MDPI 2023.

 


Description

A Matlab binary file is provided with the load records measured in the mountain bike.

  • The file has 63 columns in total.
  • The first column is the vector of time (in seconds) common to all load records
  • The other columns from 2 to 63 represent the stationary and non-stationary load record values measured on the bike normalized to zero mean and unit variance. Data comply with the acquisition described in Section 6.1.
  • Columns from 2 to 11 group the load records for Case M
  • Column 12 is the single load record for Case S
  • Columns from 13 to 42 collect the “validation” load records classified as stationary
  • The single non-stationary load record is placed in column 43
  • The remaining columns from 44 to 63 refer to the non-stationary “validation” load records.
  • For further details, contact Julian Marques

Link

zipped Matlab binary, 136MB

 


papuga@pragtic.com

Fatigue Data Repository

Databases

Weld Data

Bike in-service record

Fatigue Strength estimation via Machine Learning methods

 

 

FABER Project

 

 

pyLife workshop 2023

 

Events, Lectures

Workshops on Computational Fatigue Analysis

Workshops on Computational Fatigue Analysis 2023 - FKM Guideline Non-Linear

1st Workshop on Thermodynamics of Fatigue Process

Recorded Lectures

D&DT for Aircraft Engineers

Subscribe to our mail list


 
 
 
Development in 2011-2014 supported by:
 
TACR
 
Alfa programme