Prediction of Machine Failure by Using Machine Learning Algorithm - Fakhrurazi, Nur Amalina (IRC, 2019)
Machine failure halt many processes and causes minimum usage of unexploited resources. Prediction of the anomalies of a machine can act as an indicator and precaution to avoid machine malfunction. Prior to that, the big data undergo preprocessing; data transpose and imputation. Then, the data is cluster by using K Means to produce labeled input that will be trained by using Gradient Boosting Machine, a decision tree algorithm to make prediction. The columns consist of the variables that record the reading of machine sensor tags. Validation for the model is analyzed by using validation testing data and cross validation. Model built resulted in variables importance’s ranking and subsequently, prediction can be made. The results of the data analysis will be illustrated in a dashboard via Power BI. Consequently, the user will be able to make an informed decision.