Feature Selection for Sensor Failure Detection in Manufacturing Environment

Abstract: Automated manufacturing environments often benefit greatly from the ability to detect patterns that deviate from expected behavior. Anomaly Detection (AD) is vital in automated manufacturing to mitigate risks such as production delays, defects, and safety hazards, ensuring smooth operations and optimal productivity. AD tasks are commonly tackled using Machine Learning (ML). However, large feature sets are computationally expensive, potentially noisy and may make it challenging to understand the important factors driving the manufacturing process. To address these problems, feature selection methods are utilized. Feature selection is a technique which becomes increasingly important as high-dimensional data becomes more prevalent. In this study, our objective is to investigate how the performance of ML models trained on the Modular Ice cream Dataset on Anomalies in Sensors dataset (MIDAS) is influenced by the application of feature selection techniques. We evaluated the feature selection methods Variance Threshold (VT), F-test, χ2-test, Mutual In-formation (MI), Genetic Algorithm (GA) and Forward Selection (FS). The results showed that MI outperforms the other methods with respect to model accuracy, feature selection time and training time in Anomaly Classification (AC), but is slightly outperformed on accuracy in AD by FS. These results provide insights about feature selection methods for AD in automated manufacturing environments.