Anomaly Detection in Sensor Data with Machine Learning: Predictive Maintenance for Industrial Systems

Abstract

Anomaly detection is a critical component of predictive maintenance systems in industrial settings. By proactively identifying unusual patterns and deviations in sensor data, potential equipment failures can be predicted and mitigated before they cause costly downtime. Machine learning techniques have emerged as powerful tools for automating anomaly detection in the vast streams of sensor data generated by industrial systems. This paper provides a comprehensive review of the current state-of-the-art in machine learning-based anomaly detection for predictive maintenance, focusing on techniques applied to sensor data. We discuss the unique challenges posed by industrial sensor data, including high dimensionality, noise, and complex temporal dependencies. Popular anomaly detection algorithms, such as clustering, support vector machines, and deep learning approaches, are described, along with strategies for data preprocessing, feature engineering, and model evaluation. We also highlight recent advancements, such as the incorporation of domain knowledge and the use of incremental learning to adapt to concept drift. Finally, we discuss open challenges and future research directions to advance the field of anomaly detection for predictive maintenance in industrial systems.