{"id":{"repo_id":"gsu","oai_identifier":"oai:digitalcommons.georgiasouthern.edu:etd-1772"},"canonical_url":"https://search.dev.ndltd.org/etd/gsu/oai:digitalcommons.georgiasouthern.edu:etd-1772","repository":{"repo_id":"gsu","name":"Georgia Southern University","base_url":"https://digitalcommons.georgiasouthern.edu/do/oai/"},"display":{"title":"Ranking of Fatigue Data Based upon Monte Carlo Simulated Confidence Number Figures","abstract":"Since fatigue is probabilistic, trends observed in large populations of data are necessary to select materials, compare engineering designs, or establish preventative maintenance schedules. The generation of large experimental fatigue populations, however, is prohibitively time consuming and costly. As a solution a Weibull-based Monte Carlo simulation of fatigue life was developed based upon a failed \"bin\" model, and five billion fatigue lives were simulated. These fatigue lives were used to generate L10 lives. A model of confidence number was developed dependent upon statistically large samples of simulated L10 fatigue lives, and independent of a limited number of published curves. Using these simulated values, Confidence number figures were generated that deviated from 0.0% - 7.4% of previously published figures and were independent of confidence bands. Results differed as little as 1% from those determined graphically for experimental bearing data sets while graphical interpolation was eliminated.","abstract_html":"Since fatigue is probabilistic, trends observed in large populations of data are necessary to select materials, compare engineering designs, or establish preventative maintenance schedules. The generation of large experimental fatigue populations, however, is prohibitively time consuming and costly. As a solution a Weibull-based Monte Carlo simulation of fatigue life was developed based upon a failed "bin" model, and five billion fatigue lives were simulated. These fatigue lives were used to generate L10 lives. A model of confidence number was developed dependent upon statistically large samples of simulated L10 fatigue lives, and independent of a limited number of published curves. Using these simulated values, Confidence number figures were generated that deviated from 0.0% - 7.4% of previously published figures and were independent of confidence bands. Results differed as little as 1% from those determined graphically for experimental bearing data sets while graphical interpolation was eliminated.","abstract_has_math":false,"creators":["McBride, Jacob"],"institution":null,"degree_name":"Master of Science in Applied Engineering (M.S.A.E.)","degree_level":"Thesis (open access)","degree_discipline":"Department of Mechanical Engineering","degree_department":null,"school":null,"contributors":["David Williams","Aniruddha Mitra"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-01T07:00:00Z","date_published":"2011-05-01T07:00:00Z","updated_at":"2026-07-24T02:27:26Z","subjects":["ETD","Fatigue","Preventive maintenance","Weibull analysis","Monte Carlo analysis","Probability analysis","Rolling element bearings","Population comparison","Population ranking"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/772","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David Williams","Aniruddha Mitra"]},{"key":"dc:creator","label":"Author","values":["McBride, Jacob"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-10-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Applied Engineering (M.S.A.E.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Fatigue","Preventive maintenance","Weibull analysis","Monte Carlo analysis","Probability analysis","Rolling element bearings","Population comparison","Population ranking"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/772"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Since fatigue is probabilistic, trends observed in large populations of data are necessary to select materials, compare engineering designs, or establish preventative maintenance schedules. The generation of large experimental fatigue populations, however, is prohibitively time consuming and costly. As a solution a Weibull-based Monte Carlo simulation of fatigue life was developed based upon a failed \"bin\" model, and five billion fatigue lives were simulated. These fatigue lives were used to generate L10 lives. A model of confidence number was developed dependent upon statistically large samples of simulated L10 fatigue lives, and independent of a limited number of published curves. Using these simulated values, Confidence number figures were generated that deviated from 0.0% - 7.4% of previously published figures and were independent of confidence bands. Results differed as little as 1% from those determined graphically for experimental bearing data sets while graphical interpolation was eliminated."]},{"key":"dc:title","label":"Title","values":["Ranking of Fatigue Data Based upon Monte Carlo Simulated Confidence Number Figures"]}]}],"canonical_facts":{"dc:contributor":["David Williams","Aniruddha Mitra"],"dc:creator":["McBride, Jacob"],"dc:date.available":["2013-10-17T07:00:00Z"],"dc:description.abstract":["Since fatigue is probabilistic, trends observed in large populations of data are necessary to select materials, compare engineering designs, or establish preventative maintenance schedules. The generation of large experimental fatigue populations, however, is prohibitively time consuming and costly. As a solution a Weibull-based Monte Carlo simulation of fatigue life was developed based upon a failed \"bin\" model, and five billion fatigue lives were simulated. These fatigue lives were used to generate L10 lives. A model of confidence number was developed dependent upon statistically large samples of simulated L10 fatigue lives, and independent of a limited number of published curves. Using these simulated values, Confidence number figures were generated that deviated from 0.0% - 7.4% of previously published figures and were independent of confidence bands. Results differed as little as 1% from those determined graphically for experimental bearing data sets while graphical interpolation was eliminated."],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/772"],"dc:subject":["ETD","Fatigue","Preventive maintenance","Weibull analysis","Monte Carlo analysis","Probability analysis","Rolling element bearings","Population comparison","Population ranking"],"dc:title":["Ranking of Fatigue Data Based upon Monte Carlo Simulated Confidence Number Figures"],"thesis:degree_discipline":["Department of Mechanical Engineering"],"thesis:degree_level":["Thesis (open access)"],"thesis:degree_name":["Master of Science in Applied Engineering (M.S.A.E.)"]},"updated_at":"2026-07-24T02:27:26Z"}