Classical and Bayesian estimation of stress-strength reliability of a component having multiple states
PurposeThis article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.Design/methodology/approachThe probabilistic approach is used to obtain the reliability expression by considering the difference bet...
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| Veröffentlicht in: | The International journal of quality & reliability management 2021-02, Vol.38 (2), p.528-535 |
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| container_title | The International journal of quality & reliability management |
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| creator | K C, Siju Kumar, Mahesh Beer, Michael |
| description | PurposeThis article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.Design/methodology/approachThe probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.FindingsThe maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.Originality/valueObtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability. |
| doi_str_mv | 10.1108/IJQRM-01-2020-0009 |
| format | Article |
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The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.FindingsThe maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.Originality/valueObtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability.</description><identifier>ISSN: 0265-671X</identifier><identifier>EISSN: 1758-6682</identifier><identifier>DOI: 10.1108/IJQRM-01-2020-0009</identifier><language>eng</language><publisher>Bradford: Emerald Publishing Limited</publisher><subject>Bayesian analysis ; Component reliability ; Computation ; Maximum likelihood estimates ; Maximum likelihood estimation ; Parameter estimation ; Performance evaluation ; Random variables ; Sample size ; System reliability</subject><ispartof>The International journal of quality & reliability management, 2021-02, Vol.38 (2), p.528-535</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c317t-97ac50cb64222bd8d6660fed0581fffeec621f949f8e3ec3e1a66c007383d0933</citedby><cites>FETCH-LOGICAL-c317t-97ac50cb64222bd8d6660fed0581fffeec621f949f8e3ec3e1a66c007383d0933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/IJQRM-01-2020-0009/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,776,780,961,11614,27901,27902,52664</link.rule.ids></links><search><creatorcontrib>K C, Siju</creatorcontrib><creatorcontrib>Kumar, Mahesh</creatorcontrib><creatorcontrib>Beer, Michael</creatorcontrib><title>Classical and Bayesian estimation of stress-strength reliability of a component having multiple states</title><title>The International journal of quality & reliability management</title><description>PurposeThis article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.Design/methodology/approachThe probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.FindingsThe maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.Originality/valueObtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability.</description><subject>Bayesian analysis</subject><subject>Component reliability</subject><subject>Computation</subject><subject>Maximum likelihood estimates</subject><subject>Maximum likelihood estimation</subject><subject>Parameter estimation</subject><subject>Performance evaluation</subject><subject>Random variables</subject><subject>Sample size</subject><subject>System reliability</subject><issn>0265-671X</issn><issn>1758-6682</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkc1LxDAQxYMouH78A54CnqMzyTZtj7r4iSKKgreQTSca6bZrkhX2v7d1vQie3mHem-H9hrEjhBNEqE5vbh-f7gWgkCBBAEC9xSZYFpXQupLbbAJSF0KX-LrL9lL6GBwSUU6Yn7U2peBsy23X8HO7phRsxynlsLA59B3vPU85UkpilO4tv_NIbbDz0Ia8HseWu36x7DvqMn-3X6F744tVm8OypSFqM6UDtuNtm-jwV_fZy-XF8-xa3D1c3czO7oRTWGZRl9YV4OZ6KqWcN1WjtQZPDRQVeu-JnJbo62ntK1LkFKHV2gGUqlIN1Erts-PN3mXsP1dDCfPRr2I3nDSyUFOYolY4uOTG5WKfUiRvlnFoG9cGwYw8zQ9PA2hGnmbkOYRwE6IFRds2_2f-_EB9A5s-eTk</recordid><startdate>20210203</startdate><enddate>20210203</enddate><creator>K C, Siju</creator><creator>Kumar, Mahesh</creator><creator>Beer, Michael</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7TA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>F~G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>K6~</scope><scope>K8~</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M0T</scope><scope>M2T</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20210203</creationdate><title>Classical and Bayesian estimation of stress-strength reliability of a component having multiple states</title><author>K C, Siju ; Kumar, Mahesh ; Beer, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-97ac50cb64222bd8d6660fed0581fffeec621f949f8e3ec3e1a66c007383d0933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayesian analysis</topic><topic>Component reliability</topic><topic>Computation</topic><topic>Maximum likelihood estimates</topic><topic>Maximum likelihood estimation</topic><topic>Parameter estimation</topic><topic>Performance evaluation</topic><topic>Random variables</topic><topic>Sample size</topic><topic>System reliability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>K C, Siju</creatorcontrib><creatorcontrib>Kumar, Mahesh</creatorcontrib><creatorcontrib>Beer, Michael</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Materials Business File</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Business Collection</collection><collection>DELNET Management Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Global</collection><collection>Healthcare Administration Database</collection><collection>Telecommunications Database</collection><collection>Engineering Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>The International journal of quality & reliability management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>K C, Siju</au><au>Kumar, Mahesh</au><au>Beer, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classical and Bayesian estimation of stress-strength reliability of a component having multiple states</atitle><jtitle>The International journal of quality & reliability management</jtitle><date>2021-02-03</date><risdate>2021</risdate><volume>38</volume><issue>2</issue><spage>528</spage><epage>535</epage><pages>528-535</pages><issn>0265-671X</issn><eissn>1758-6682</eissn><abstract>PurposeThis article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.Design/methodology/approachThe probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. 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| subjects | Bayesian analysis Component reliability Computation Maximum likelihood estimates Maximum likelihood estimation Parameter estimation Performance evaluation Random variables Sample size System reliability |
| title | Classical and Bayesian estimation of stress-strength reliability of a component having multiple states |
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