Towards Industry 4.0: Machine malfunction prediction based on IIoT streaming data

Abstract

The manufacturing industry relies on continuous optimization to meet quality and safety standards, which is part of the Industry 4.0 concept. Predicting when a specific part of a product will fail to meet these standards is of utmost importance and requires vast amounts of data, which often are collected from variety of sensors, often reffered to as Industrial Internet of Things (IIoT). Using a published dataset from Bosch, that describes the process at every step of production, we aim to train a machine learning model that can accurately predict faults in the manufacturing process. The dataset provides two years of production data across four production lines and 52 stations. Considering that the data generated from each production part includes more than four thousand features, we investigate various feature selection and data preprocessing methods. The obtained results exhibit Area Under the Receiver Operating Characteristic Curve (AUC ROC) of up to 0.997, which is remarkable and promising even for real-life production use.