Predictive Maintenance Using Linear Regression

Abstract

Problems regarding machine damage often occur in many industries, especially the manufacturing industry, which causes large losses for companies. This is of course influenced by various factors such as engine temperatures that are too high, engine rotation that is too fast, poor engine torque values, and so on. This research aims to provide predictive analysis results regarding engine conditions that have the potential to experience damage. To achieve this goal, this research will carry out predictive maintenance analysis using a linear regression analysis approach in which two linear regression models will be carried out where the first model involves PCA preprocessing and the second model is carried out without PCA. This research will use the predictive maintenance dataset from the conference (Matzka, 2020). It is known that the MSE, RMSE, MAE, and R2 values of the two methods have the same values, namely 0.909, 0.953, 0.806, and 0.772 respectively. Based on this research, it is concluded that whether PCA is performed or not, it does not significantly affect the results of the regression analysis. This outcome can be attributed to the artificial nature of the dataset, rendering it ideal. Moreover, the retained PCA value of 98% is close to the number of attributes in the original dataset.