Acceso abierto

Weibull or Lognormal Distribution to Characterize Fatigue Life Scatter – Which is More Suitable? – Continued

Rob Plaskitt

Rob Plaskitt

Hottinger Bruel & Kjaer LtdUnited Kingdom
Perfil de Orcid
Buscar este autor en
Sciendo|Google Scholar
Plaskitt, Rob
, 
Chris Wynn-Jones

Chris Wynn-Jones

Hottinger Bruel & Kjaer LtdUnited Kingdom
Perfil de Orcid
Buscar este autor en
Sciendo|Google Scholar
Wynn-Jones, Chris
 y 
Andrew Halfpenny

Andrew Halfpenny

Hottinger Bruel & Kjaer LtdUnited Kingdom
Perfil de Orcid
Buscar este autor en
Sciendo|Google Scholar
Halfpenny, Andrew
  
07 jul 2025
Weibull or Lognormal Distribution to Characterize Fatigue Life Scatter – Which is More Suitable? – Continued's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Categoría del artículo: Research Article
Publicado en línea: 07 jul 2025
DOI: https://doi.org/10.2478/fas-2024-0011
Palabras clave
© 2025 Rob Plaskitt et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1.

Lognormal and 3-parameter Weibull distribution of Schijve dataset fatigue test results a) reproduced from Schijve (2009, figure 12.3), and b) reanalysis for this paper.
Lognormal and 3-parameter Weibull distribution of Schijve dataset fatigue test results a) reproduced from Schijve (2009, figure 12.3), and b) reanalysis for this paper.

Figure 2.

2-parameter Weibull distribution of Schijve dataset fatigue test results a) reproduced from Brot (2017, figure 18), and b) reanalysis for this paper.
2-parameter Weibull distribution of Schijve dataset fatigue test results a) reproduced from Brot (2017, figure 18), and b) reanalysis for this paper.

Figure 3.

Lognormal distribution of Schijve dataset fatigue test results a) reproduced from Brot (2017, figure 19), and b) reanalysis for this paper.
Lognormal distribution of Schijve dataset fatigue test results a) reproduced from Brot (2017, figure 19), and b) reanalysis for this paper.

Figure 4.

Lognormal and Weibull distribution of Schijve dataset fatigue test results a) with 3-parameter Weibull as used in Schijve analysis (2009), and b) with 2-parameter Weibull as used in Brot analysis in (2017).
Lognormal and Weibull distribution of Schijve dataset fatigue test results a) with 3-parameter Weibull as used in Schijve analysis (2009), and b) with 2-parameter Weibull as used in Brot analysis in (2017).

Figure 5.

Lognormal and 2- and 3-parameter Weibull distributions of HBK dataset (logarithmic x-axis with numbers excluded to maintain data confidentiality).
Lognormal and 2- and 3-parameter Weibull distributions of HBK dataset (logarithmic x-axis with numbers excluded to maintain data confidentiality).

Figure 6.

Lognormal and 2- and 3-parameter Weibull distributions of HBK dataset with confidence bounds for the lower tail of the distributions between 0.01% and 50% (logarithmic x-axis with numbers excluded to maintain data confidentiality).
Lognormal and 2- and 3-parameter Weibull distributions of HBK dataset with confidence bounds for the lower tail of the distributions between 0.01% and 50% (logarithmic x-axis with numbers excluded to maintain data confidentiality).

Fatigue test results in Kilo Cycles (specimen number)_

636 (13G) 1160 (13J) 1461 (12F)
707 (13H) 1167 (12H) 1511 (13C)
801 (13K) 1262 (13A) 2119 (12C)
1038 (12K) 1265 (13B) 2132 (13D)
1090 (13E) 1404 (12G) 2158 (12D)
1102 (12J) 1418 (13F) 2368 (12E)

Extrapolated fatigue life normalised to γ (gamma) at probability of failure (reliability)_

% Probability of failure (% reliability) Lognormal 2-parameter Weibull 3-parameter Weibull
1% (99%) 1.383 0.786 1.293
0.1% (99.9%) 0.584 0.130 1.029
0.01% (99.99%) 0.288 0.022 1.003
0.001% (99.999%) 0.155 0.004 1.000
0.0001% (99.9999%) 0.089 0.001 1.000

Extrapolated fatigue life (Kilo Cycles) at probability of failure at 0_01% (=99_99% reliability)_

Distribution Figure 2a distribution fit Figure 2b distribution fit
Weibull 86 86.3
Lognormal 300 303.4

Extrapolated fatigue life normalised to γ(gamma) at probability of failure (reliability)_

% Probability of failure (% reliability) Lognormal 2-parameter Weibull 3-parameter Weibull
1% (99%) 1.036 0.723 1.157
0.1% (99.9%) 0.770 0.352 1.044
0.01% (99.99%) 0.603 0.171 1.013
0.001% (99.999%) 0.487 0.084 1.004
0.0001% (99.9999%) 0.403 0.041 1.001

Extrapolated fatigue life (Kilo Cycles) at probability of failure (reliability)_

% Probability of failure (% reliability) Lognormal 2-parameter Weibull 3-parameter Weibull γ (gamma) = 503.4
1% (99%) 521.7 363.8 582.5
0.1% (99.9%) 387.5 177.1 525.7
0.01% (99.99%) 303.4 86.3 509.7
0.001% (99.999%) 245.3 42.1 505.2
0.0001% (99.9999%) 202.8 20.5 503.9
Idioma:
Inglés
Calendario de la edición:
1 veces al año
Temas de la revista:
Ingeniería, Introducciones y reseñas, Ingeniería, otros
RSS Feed de revista