dc.contributor.authorNaganathan, Arvind
dc.description.abstractThe time for the occurrence of failure in a machine has been predicted using a Weibull model. The model uses the information of past failures and fits it into a probability distribution that yields a prediction of future failures. The operational data used for analysis is a series of failure times procured from an industrial machine used in a manufacturing system. This thesis discusses three methods of parametric estimation of the Weibull distribution, namely the maximum likelihood estimation, the method of moments, and the least squares method, and compares their errors in estimation and develops a graphical approach to help choose the right method for the right application. In addition, for the maximum likelihood estimation method, we modify the data set into an interval censored set of data and estimate the parameters for various observation lengths. The error for various observation lengths has been plotted and a tradeoff is developed between inspection load and error. This helps to choose an optimal value of the observation length. Finally, a time-to-failure prediction based on the estimated parameters is done.en_US
dc.format.extent73 p.en_US
dc.titleFailure prediction techniques based on Weibull modelen_US
dc.contributor.supervisorEr Meng Jooen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US

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