Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?
Background Life cycle prediction measures, that provide information on the probability of failure of equipments, have been applied in electronic and mechanical engineering and for predicting the strength of dental implants. However, the same has not been utilized as yet in medical equipment such as...
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| Veröffentlicht in: | Clinical chemistry and laboratory medicine 2018-11, Vol.56 (12), p.2067-2071 |
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| creator | Verma, Alekh Narula, Aastha Katyal, Akshi Yadav, Shakti Kumar Anand, Priyanka Jahan, Aarzoo Pruthi, Sonam Kumar Sarin, Namrata Gupta, Ruchika Singh, Sompal |
| description | Background Life cycle prediction measures, that provide information on the probability of failure of equipments, have been applied in electronic and mechanical engineering and for predicting the strength of dental implants. However, the same has not been utilized as yet in medical equipment such as hematology analyzers. Methods Failure data of five automated hematology analyzers (3-part differential) was collected over 14 consecutive months and a Weibull probability plot was made. The scale and shape parameters of this plot were used to predict failure probability distribution. This was then combined with various costs involved in remedial maintenance to get a cost analysis. Results The analyzers in their "useful life" period were found to suffer fewer actual and predicted failures compared to those in the "wear out" phase. Cost analysis showed a considerably higher per month cost of remedial maintenance of analyzers compared to the price of a comprehensive maintenance contract. Conclusions Our study demonstrates, for the first time, that Weibull distribution can be applied well to hematology analyzers for modeling of failure data and the resultant information is helpful in the cost analysis of maintenance to allow for prudent and informed decision making with regards to the mode of maintenance of analyzers. |
| doi_str_mv | 10.1515/cclm-2018-0569 |
| format | Article |
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However, the same has not been utilized as yet in medical equipment such as hematology analyzers. Methods Failure data of five automated hematology analyzers (3-part differential) was collected over 14 consecutive months and a Weibull probability plot was made. The scale and shape parameters of this plot were used to predict failure probability distribution. This was then combined with various costs involved in remedial maintenance to get a cost analysis. Results The analyzers in their "useful life" period were found to suffer fewer actual and predicted failures compared to those in the "wear out" phase. Cost analysis showed a considerably higher per month cost of remedial maintenance of analyzers compared to the price of a comprehensive maintenance contract. Conclusions Our study demonstrates, for the first time, that Weibull distribution can be applied well to hematology analyzers for modeling of failure data and the resultant information is helpful in the cost analysis of maintenance to allow for prudent and informed decision making with regards to the mode of maintenance of analyzers.</description><identifier>ISSN: 1434-6621</identifier><identifier>EISSN: 1437-4331</identifier><identifier>DOI: 10.1515/cclm-2018-0569</identifier><identifier>PMID: 30089095</identifier><language>eng</language><publisher>Germany: De Gruyter</publisher><subject>analyzer ; Analyzers ; Automation ; Cost analysis ; Decision analysis ; Decision making ; Dental implants ; Dental prosthetics ; Dental restorative materials ; Failure ; Failure analysis ; failure rate ; Failure rates ; Hematology ; Hematology - instrumentation ; Humans ; Life cycles ; Maintenance ; Mechanical engineering ; Medical equipment ; prediction ; Predictions ; Predictive Value of Tests ; Probability distribution ; Prospective Studies ; Statistical analysis ; Surgical implants ; Weibull distribution</subject><ispartof>Clinical chemistry and laboratory medicine, 2018-11, Vol.56 (12), p.2067-2071</ispartof><rights>2018 Walter de Gruyter GmbH, Berlin/Boston</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-e1f55161240dda677b24a830cc503a81d4df14660154328840ad0ef0c94dedeb3</citedby><cites>FETCH-LOGICAL-c471t-e1f55161240dda677b24a830cc503a81d4df14660154328840ad0ef0c94dedeb3</cites><orcidid>0000-0003-0235-063X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.degruyter.com/document/doi/10.1515/cclm-2018-0569/pdf$$EPDF$$P50$$Gwalterdegruyter$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.degruyter.com/document/doi/10.1515/cclm-2018-0569/html$$EHTML$$P50$$Gwalterdegruyter$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27901,27902,66497,68281</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30089095$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Verma, Alekh</creatorcontrib><creatorcontrib>Narula, Aastha</creatorcontrib><creatorcontrib>Katyal, Akshi</creatorcontrib><creatorcontrib>Yadav, Shakti Kumar</creatorcontrib><creatorcontrib>Anand, Priyanka</creatorcontrib><creatorcontrib>Jahan, Aarzoo</creatorcontrib><creatorcontrib>Pruthi, Sonam Kumar</creatorcontrib><creatorcontrib>Sarin, Namrata</creatorcontrib><creatorcontrib>Gupta, Ruchika</creatorcontrib><creatorcontrib>Singh, Sompal</creatorcontrib><title>Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?</title><title>Clinical chemistry and laboratory medicine</title><addtitle>Clin Chem Lab Med</addtitle><description>Background Life cycle prediction measures, that provide information on the probability of failure of equipments, have been applied in electronic and mechanical engineering and for predicting the strength of dental implants. However, the same has not been utilized as yet in medical equipment such as hematology analyzers. Methods Failure data of five automated hematology analyzers (3-part differential) was collected over 14 consecutive months and a Weibull probability plot was made. The scale and shape parameters of this plot were used to predict failure probability distribution. This was then combined with various costs involved in remedial maintenance to get a cost analysis. Results The analyzers in their "useful life" period were found to suffer fewer actual and predicted failures compared to those in the "wear out" phase. Cost analysis showed a considerably higher per month cost of remedial maintenance of analyzers compared to the price of a comprehensive maintenance contract. Conclusions Our study demonstrates, for the first time, that Weibull distribution can be applied well to hematology analyzers for modeling of failure data and the resultant information is helpful in the cost analysis of maintenance to allow for prudent and informed decision making with regards to the mode of maintenance of analyzers.</description><subject>analyzer</subject><subject>Analyzers</subject><subject>Automation</subject><subject>Cost analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Dental implants</subject><subject>Dental prosthetics</subject><subject>Dental restorative materials</subject><subject>Failure</subject><subject>Failure analysis</subject><subject>failure rate</subject><subject>Failure rates</subject><subject>Hematology</subject><subject>Hematology - instrumentation</subject><subject>Humans</subject><subject>Life cycles</subject><subject>Maintenance</subject><subject>Mechanical engineering</subject><subject>Medical equipment</subject><subject>prediction</subject><subject>Predictions</subject><subject>Predictive Value of Tests</subject><subject>Probability distribution</subject><subject>Prospective Studies</subject><subject>Statistical analysis</subject><subject>Surgical implants</subject><subject>Weibull distribution</subject><issn>1434-6621</issn><issn>1437-4331</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNptkctrFjEUxYMo9qFblxJw42Zq3pPPhUVKq0LBjeJyyCR3akpmMs0D-fzrzfSrCuLqnsXvnvs4CL2g5IxKKt9YG-aOEao7ItXuETqmgved4Jw-vteiU4rRI3SS8y0hVErRP0VHnBC9Izt5jMKV8aEmwMkUwGsC523xccFxwnBX_TrDUt5iaxb8DfxYQ8DO55KavMdGwGZdgweHS8Smljg3I4e_Q6sxxJs9NosJ-5-Q8vkz9GQyIcPzh3qKvl5dfrn42F1__vDp4v11Z0VPSwd0kpIqygRxzqi-H5kwmhNrJeFGUyfcRIVS7RzBmdaCGEdgInYnHDgY-Sl6ffBdU7yrkMsw-2whBLNArHlgRCsmNaG0oa_-QW9jTW3jRm2YZEpv1NmBsinmnGAa1uRnk_YDJcOWw7DlMGw5DFsOreHlg20dZ3B_8N-Pb8C7A_DDhALJwU2q-yb-jv-_s2xvYUT1_BdRupiW</recordid><startdate>20181127</startdate><enddate>20181127</enddate><creator>Verma, Alekh</creator><creator>Narula, Aastha</creator><creator>Katyal, Akshi</creator><creator>Yadav, Shakti Kumar</creator><creator>Anand, Priyanka</creator><creator>Jahan, Aarzoo</creator><creator>Pruthi, Sonam Kumar</creator><creator>Sarin, Namrata</creator><creator>Gupta, Ruchika</creator><creator>Singh, Sompal</creator><general>De Gruyter</general><general>Walter De Gruyter & Company</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0235-063X</orcidid></search><sort><creationdate>20181127</creationdate><title>Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?</title><author>Verma, Alekh ; Narula, Aastha ; Katyal, Akshi ; Yadav, Shakti Kumar ; Anand, Priyanka ; Jahan, Aarzoo ; Pruthi, Sonam Kumar ; Sarin, Namrata ; Gupta, Ruchika ; Singh, Sompal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c471t-e1f55161240dda677b24a830cc503a81d4df14660154328840ad0ef0c94dedeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>analyzer</topic><topic>Analyzers</topic><topic>Automation</topic><topic>Cost analysis</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Dental implants</topic><topic>Dental prosthetics</topic><topic>Dental restorative materials</topic><topic>Failure</topic><topic>Failure analysis</topic><topic>failure rate</topic><topic>Failure rates</topic><topic>Hematology</topic><topic>Hematology - instrumentation</topic><topic>Humans</topic><topic>Life cycles</topic><topic>Maintenance</topic><topic>Mechanical engineering</topic><topic>Medical equipment</topic><topic>prediction</topic><topic>Predictions</topic><topic>Predictive Value of Tests</topic><topic>Probability distribution</topic><topic>Prospective Studies</topic><topic>Statistical analysis</topic><topic>Surgical implants</topic><topic>Weibull distribution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verma, Alekh</creatorcontrib><creatorcontrib>Narula, Aastha</creatorcontrib><creatorcontrib>Katyal, Akshi</creatorcontrib><creatorcontrib>Yadav, Shakti Kumar</creatorcontrib><creatorcontrib>Anand, Priyanka</creatorcontrib><creatorcontrib>Jahan, Aarzoo</creatorcontrib><creatorcontrib>Pruthi, Sonam Kumar</creatorcontrib><creatorcontrib>Sarin, Namrata</creatorcontrib><creatorcontrib>Gupta, Ruchika</creatorcontrib><creatorcontrib>Singh, Sompal</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical chemistry and laboratory medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verma, Alekh</au><au>Narula, Aastha</au><au>Katyal, Akshi</au><au>Yadav, Shakti Kumar</au><au>Anand, Priyanka</au><au>Jahan, Aarzoo</au><au>Pruthi, Sonam Kumar</au><au>Sarin, Namrata</au><au>Gupta, Ruchika</au><au>Singh, Sompal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?</atitle><jtitle>Clinical chemistry and laboratory medicine</jtitle><addtitle>Clin Chem Lab Med</addtitle><date>2018-11-27</date><risdate>2018</risdate><volume>56</volume><issue>12</issue><spage>2067</spage><epage>2071</epage><pages>2067-2071</pages><issn>1434-6621</issn><eissn>1437-4331</eissn><abstract>Background Life cycle prediction measures, that provide information on the probability of failure of equipments, have been applied in electronic and mechanical engineering and for predicting the strength of dental implants. However, the same has not been utilized as yet in medical equipment such as hematology analyzers. Methods Failure data of five automated hematology analyzers (3-part differential) was collected over 14 consecutive months and a Weibull probability plot was made. The scale and shape parameters of this plot were used to predict failure probability distribution. This was then combined with various costs involved in remedial maintenance to get a cost analysis. Results The analyzers in their "useful life" period were found to suffer fewer actual and predicted failures compared to those in the "wear out" phase. Cost analysis showed a considerably higher per month cost of remedial maintenance of analyzers compared to the price of a comprehensive maintenance contract. Conclusions Our study demonstrates, for the first time, that Weibull distribution can be applied well to hematology analyzers for modeling of failure data and the resultant information is helpful in the cost analysis of maintenance to allow for prudent and informed decision making with regards to the mode of maintenance of analyzers.</abstract><cop>Germany</cop><pub>De Gruyter</pub><pmid>30089095</pmid><doi>10.1515/cclm-2018-0569</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-0235-063X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | analyzer Analyzers Automation Cost analysis Decision analysis Decision making Dental implants Dental prosthetics Dental restorative materials Failure Failure analysis failure rate Failure rates Hematology Hematology - instrumentation Humans Life cycles Maintenance Mechanical engineering Medical equipment prediction Predictions Predictive Value of Tests Probability distribution Prospective Studies Statistical analysis Surgical implants Weibull distribution |
| title | Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers? |
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