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Maintenance costs and machine availability are the most important concerns of any company that owns large machinery, especially gas turbine engines. With the advent of the 4th wave of the industrial revolution also known as Industrial Internet of Things (IIoT), the focus has shifted onto optimal utilization of the equipment. Reduction in the installation costs of sensors helped companies to install them on their key equipment. The live data from the sensors can now be utilized to monitor the health of the machines. This thesis proposes a prognostic technique to predict time-tofailure of gas turbine engines using standard machine learning and deep learning techniques that puts the data from sensors to good use. Our proposed approach provides accurate feedback on the TABLE OF CONTENTS

and results, part B, where the DTLZ2 function is defined, the third line in the equations should be 0≤xi≤1, not 0≤x1, x2≤1. 2-In section VI-Application and results, part B, the first line after defining the equations of DTLZ2 function, the correct form is k=n-m+1 not k=n+m−1. 3-Page 9, the last line, the first word should be corrected to ParEGO instead of ParEgo.