A Modular Ice Cream Factory Dataset on Anomalies in Sensors to Support Machine Learning Research in Manufacturing Systems.

A small deviation in manufacturing systems can cause huge economic losses, and all components and sensors in the system must be continuously monitored to provide an immediate response. The usual industrial practice is rather simplistic based on brute force checking of limited set of parameters often with pessimistic pre-defined bounds. The usage of appropriate machine learning techniques can be very valuable in this context to narrow down the set of parameters to monitor, define more refined bounds, and forecast impending issues. One of the factors hampering progress in this field is the lack of datasets that can realistically mimic the behaviours of manufacturing systems. In this paper, we propose a new dataset called MIDAS (Modular Ice cream factory Dataset on Anomalies in Sensors) to support machine learning research in analog sensor data. MIDAS is created using a modular manufacturing simulation environment that simulates the ice cream-making process. Using MIDAS, we evaluated four different supervised machine learning algorithms (Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron) for two different problems: anomaly detection and anomaly classification. The results showed that multilayer perceptron is the most suitable algorithm with respect to model accuracy and execution time. We have made the data set and the code for the experiments publicly available, to enable interested researchers to enhance the state of the art by conducting further studies.