Applying Predictive Analytics in a Continuous Process Industry

Abstract

In this paper an attempt is made to develop data driven models on pilot data set for predicting fault in machines of continuous process industry on various selected attributes using techniques of Multiple Linear Regression Model (MLR), Regression Tree (RT) and Artificial Neural Networks (ANN). Association rules are also derived from the available data set. Efforts are also made to predict total shutdown time of machines. These machines are used for manufacturing components machined for Heavy Commercial Vehicles (HCV), Light Commercial Vehicles (LCV), Multi Axle Vehicle (MA) and Tractors. To check the robustness of models a comparison is made between the results derived from various techniques discussed above. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values of down time. Based on actual and predicted results various error scores are calculated to evaluate best model and check robustness of the models under study. Training and validation of the model is done using datasets collected from a manufacturing unit located at Pithampur industrial area near Indore, Madhya Pradesh, India. In the current paper an association is also developed between the attributes and occurrence of the fault. The developed model will be used on the bigger data set which will help the stakeholders of the organization for smooth functioning of the unit and for better governance in the organization. XLMiner is used for model development and simulations. After analysis results show that ANN, RT and Association Rule techniques are capable of capturing the data set.