Predictive Analytic on Machine Failure by Utilizing Linear Regression (Historical Data) - MOHD SAMLAN, NUR NASHA AYUNI (IRC, 2020)
“Upstream oil and gas is the part of the petroleum industry that locates and produces crude oil and natural gas through a network of pumps and wells.” Few main problems in this field are inefficiencies and machine breakdown that occur when wells are not being optimally engaged, as well as production that stops temporarily when parts and equipment fail and waiting to be repaired. Machine failure refers to any event in which it cannot fulfil its mission or task. It may also mean that the machine has stopped working, is not working properly, or does not meet target expectations. In addition, the machine mentioned here is referring to turbine generator in oil and gas industry or PETRONAS itself. Turbine generator is a connection of a shaft of a steam turbine or gas turbine engine connected to a high-speed electric generator to generate electricity. Turbine generators are a very important necessity for the oil and gas industry. These generators provide key energy sources to the industry, in particular to assist in drilling and digging. Drilling and digging procedures are key to these industries, and it takes a lot of energy to service heavy equipment. Somehow, the industry has been experiencing machine failure for years, resulting in interfering with the smoothness of their production and increase cost of repair after a breakdown. Hence, the purpose of this study is to predict the machine upcoming failure by utilizing predictive analytic on machine learning where the process involves linear regression algorithm, therefore to create a model on KNIME analytic platform. Upon measuring the accuracy of the predicted model, the result will be displayed as a dashboard for the user to monitor the condition of the machine state in Power Business Intelligence.