Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/65061
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dc.contributor.authorShah Parshva Dikulbhai-
dc.date.accessioned2015-06-11T06:58:54Z-
dc.date.available2015-06-11T06:58:54Z-
dc.date.copyright2014en_US
dc.date.issued2014-
dc.identifier.urihttp://hdl.handle.net/10356/65061-
dc.description.abstractData acquisition of various Downtime Reasons in OEE has always been a major issue. The age-old manual production reports provided few detail s on productivity and availability. It provided no details on how or why the production line did not meet the OEE benchmark s. Sometimes the reports for each shift are subjective and arc often conflicting. They also furnish incomplete information on the reasons for lost avail ability. Hence there was a need to devise a new method for the data acquisition that was both competent and efficient in improving OEE metric. In the scope of the current project, the issue discussed above is tackled with the help of automatic data acquisition of the downtime reason codes. A GUI is developed on an HMI featuring various downtime reason codes. The HMI will be mounted in the field. As soon as the machine is stopped, the operator is expected to press the appropriate reason code. The HMI will then automatically log the timestamp and the reason codes each time they are pressed and also create a database. The database will be automatically logged on to the thumb drive that is attached to the HMl. The data collected in the database is to be analysed in order to reduce downtime and improve OEE. In this project, various methods of analysing data such as the Fish bone Analysis and What-If analysis arc carried out.en_US
dc.format.extent69 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleImproving OEE with automatic data acquisition and analysis of total downtime lossesen_US
dc.typeThesis
dc.contributor.supervisorWang Dan Weien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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