Machine Learning for Anomaly Detection in Industrial Environments

Abstract

In modern industry, anomaly detection is an important part of safety and productivity management. Early anomaly detection could allow for timely interventions, preventing malfunctions and reducing risks for human workers and machines. This work aims to deliver an overview of the use of machine learning for anomaly detection in industrial environments, highlight the state-of-the-art, and discuss challenges and prospects for future research. Existing approaches, methodologies, and results related to anomaly detection are summarized, focusing on the application of machine learning for different types of industrial anomalies. Research findings indicate that, despite the current advances, there is still room for improvements and developments in machine learning-based anomaly detection in industrial environments, designating an important future field of research.

Keywords

• machine learning
• anomaly detection
• industrial environments
• fault detection