Abstract
Value of time has become an important perspective in business application ranging from day to day working to big businesses. The value of time is more important in the case of refinery business which has become of paramount importance with increasing energy needs. The main point of contention in refinery operations is the periodic maintenance of the pipelines which consumes of valuable time and resources. With a proper solution which can cater the time requirements of the lead time. The fact is that time consumption is extremely critical for the operations of refinery. Therefore, the application of machine learning is implemented in the prediction of when and how the equipment will be needing preventive maintenance all of this can be accomplished by using available open-source data which will help us in the designing the algorithm and also in the learning of the same. This model has allowed us to investigate different outcomes and planning strategies that are possible through the prediction models and the estimated timings for the maintenance of the pipelines. This predictive maintenance system has allowed for more intelligent and smart planning and has reduced the down time significantly allowing for more revenues.
Publication Date
12-2022
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Ehsan Warriach
Recommended Citation
Alblooshi, Shaima, "Predicting Failure Rate of Oil & Gas Equipment Using ML" (2022). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11379
Campus
RIT Dubai