Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment

Today, modern industrial equipment is very complex as it involves
sophisticated assets and systems. Thus, machine equipment optimization and safety
have become operators' main concerns in the quest for maintaining optimum
operational efficiency, asset availability, safety and cost-effective. Due to its
complexity of the internal structure of the equipment, engineers are often faced with
large amounts of information called multivariate datasets which are hard to understand
by human nature. This led to difficulty in achieving high accuracy prediction of the
equipment and decision-making is hard to achieve. Thus, an organization unable to
decide whether to purchase new equipment or provide maintenance strategies. Hence,
the purpose of this research is to develop a machine learning workflow model of the
integration between Alteryx tools to do prediction of RUL using “real world”
multivariate dataset in Oil and Gas industry, and Microsoft Power BI to visualize the
result of the prediction for a better insight. One of the most popular machine learning
approaches is employed in the prediction workflow which is the Artificial Neural
Network (ANN) algorithm, due to its capability to learn from a large volume of data
points and high prediction accuracy. Performances of the accuracy of prediction
workflow were measured using root mean squared error (RMSE).