1 / 7

Industrial Project (234313) Tube Lifetime Predictive Algorithm

COMPUTER SCIENCE DEPARTMENT Technion - Israel Institute of Technology. July 8, 2012. Industrial Project (234313) Tube Lifetime Predictive Algorithm. Students: Nidal Hurani , Ghassan Ibrahim Supervisor: Shai Rozenrauch. Goals .

micheal
Download Presentation

Industrial Project (234313) Tube Lifetime Predictive Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. COMPUTER SCIENCE DEPARTMENT Technion - Israel Institute of Technology July 8, 2012 Industrial Project (234313)Tube Lifetime Predictive Algorithm Students:Nidal Hurani, Ghassan Ibrahim Supervisor:ShaiRozenrauch

  2. Goals • Finding tube lifetime predictive algorithm based on parameters and results of the CT Radar system • The algorithm target is to predict with a precision of 75% the lifetime of the tubes • Algorithm implementation

  3. Obstacles • Raw data was not reliable • Completing the missing data in order to use it correctly • Finding parameters and measures which influence the most of the lifetime of the tube • Fit to a known statistical model which can describe the tube lifetime given these parameters • Dealing with huge data

  4. Methodology • Run queries over the database (SQL) to retrieve the relevant data set • Processing and transforming the data into a training set which is used later in the predictive algorithm • Building a windows form application which can “talk “ with R • Fitting a decision tree using CART ( Classification and Regression Tree) for the giving training set • Predict a tube lifetime given a vector of estimated parameters or measures

  5. Environments &Technologies • Main programming language - C# • IDE - Visual studio 2010 • Statistical tool JMP 7 - for finding possible statistical models which can describe the problem • EXCEL (MS office) • R (Statistical Language) • RCOM • MSSQL • JMP 7

  6. Achievements • A predictor with ±120 days error in general • 76.8293% of the predictions with ±60 days error • User friendly program

  7. Conclusions • The more the training set reflect the tube real behavior the more accurate the algorithm shall predict • Depends for example on the way of completing the data & also the amount of data needs to be complete • Having a comprehensive training set gives more accurate results • The algorithm somehow is “flexible” • Whenever a new parameter is recognized as a huge influencer

More Related