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Industrial Project (234313) Tube Lifetime Predictive Algorithm

This industrial project focused on creating a predictive algorithm to estimate tube lifetimes with 75% precision. Challenges included unreliable raw data and huge datasets. Methodology involved SQL queries, data processing, decision tree fitting, and software development. Achieved predictor with ±120 days error, user-friendly interface, and high prediction accuracy. Conclusion emphasizes the importance of accurate training data and flexibility of algorithm. Developed using C#, Visual Studio, JMP 7, R, MSSQL.

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Industrial Project (234313) Tube Lifetime Predictive Algorithm

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  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

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